In the ever-expanding sphere of assistive robotics, the pressing need for advanced methods capable of accurately tracking individuals within unstructured indoor settings has been magnified. This research endeavours to devise a realtime visual tracking mechanism that encapsulates high performance attributes while maintaining minimal computational requirements. Inspired by the neural processes of the human brain’s visual information handling, our innovative algorithm employs a pattern image, serving as an ephemeral memory, which facilitates the identification of motion within images. This tracking paradigm was subjected to rigorous testing on a Nao humanoid robot, demonstrating noteworthy outcomes in controlled laboratory conditions. The algorithm exhibited a remarkably low false detection rate, less than 4%, and target losses were recorded in merely 12% of instances, thus attesting to its successful operation. Moreover, the algorithm’s capacity to accurately estimate the direct distance to the target further substantiated its high efficacy. These compelling findings serve as a substantial contribution to assistive robotics. The proficient visual tracking methodology proposed herein holds the potential to markedly amplify the competencies of robots operating in dynamic, unstructured indoor settings, and set the foundation for a higher degree of complex interactive tasks.
This document describes a human-tracking robot that uses ultra-wideband (UWB) technology. It proposes using a modified hyperbolic positioning algorithm and virtual spring model to detect, locate, and track a target person in real-time. The robot is equipped with UWB anchors and ultrasound sensors, and an embedded control board processes sensor data to drive motors for following movements. An advantage of the UWB method over computer vision is robustness to varying lighting conditions outdoors. Experimental results demonstrate the tracking performance of the robot.
Scheme for motion estimation based on adaptive fuzzy neural networkTELKOMNIKA JOURNAL
Many applications of robots in collaboration with humans require the robot to follow the person autonomously. Depending on the tasks and their context, this type of tracking can be a complex problem. The paper proposes and evaluates a principle of control of autonomous robots for applications of services to people, with the capacity of prediction and adaptation for the problem of following people without the use of cameras (high level of privacy) and with a low computational cost. A robot can easily have a wide set of sensors for different variables, one of the classic sensors in a mobile robot is the distance sensor. Some of these sensors are capable of collecting a large amount of information sufficient to precisely define the positions of objects (and therefore people) around the robot, providing objective and quantitative data that can be very useful for a wide range of tasks, in particular, to perform autonomous tasks of following people. This paper uses the estimated distance from a person to a service robot to predict the behavior of a person, and thus improve performance in autonomous person following tasks. For this, we use an adaptive fuzzy neural network (AFNN) which includes a fuzzy neural network based on Takagi-Sugeno fuzzy inference, and an adaptive learning algorithm to update the membership functions and the rule base. The validity of the proposal is verified both by simulation and on a real prototype. The average RMSE of prediction over the 50 laboratory tests with different people acting as target object was 7.33.
Acoustic event characterization for service robot using convolutional networksIJECEIAES
This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcscpconf
This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.
Robot operating system based autonomous navigation platform with human robot ...TELKOMNIKA JOURNAL
In emerging technologies, indoor service robots are playing a vital role for people who are physically challenged and visually impaired. The service robots are efficient and beneficial for people to overcome the challenges faced during their regular chores. This paper proposes the implementation of autonomous navigation platforms with human-robot interaction which can be used in service robots to avoid the difficulties faced in daily activities. We used the robot operating system (ROS) framework for the implementation of algorithms used in auto navigation, speech processing and recognition, and object detection and recognition. A suitable robot model was designed and tested in the Gazebo environment to evaluate the algorithms. The confusion matrix that was created from 125 different cases points to the decent correctness of the model.
Obstacle detection for autonomous systems using stereoscopic images and bacte...IJECEIAES
This paper presents a low cost strategy for real-time estimation of the position of ob- stacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcsandit
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
This document describes a human-tracking robot that uses ultra-wideband (UWB) technology. It proposes using a modified hyperbolic positioning algorithm and virtual spring model to detect, locate, and track a target person in real-time. The robot is equipped with UWB anchors and ultrasound sensors, and an embedded control board processes sensor data to drive motors for following movements. An advantage of the UWB method over computer vision is robustness to varying lighting conditions outdoors. Experimental results demonstrate the tracking performance of the robot.
Scheme for motion estimation based on adaptive fuzzy neural networkTELKOMNIKA JOURNAL
Many applications of robots in collaboration with humans require the robot to follow the person autonomously. Depending on the tasks and their context, this type of tracking can be a complex problem. The paper proposes and evaluates a principle of control of autonomous robots for applications of services to people, with the capacity of prediction and adaptation for the problem of following people without the use of cameras (high level of privacy) and with a low computational cost. A robot can easily have a wide set of sensors for different variables, one of the classic sensors in a mobile robot is the distance sensor. Some of these sensors are capable of collecting a large amount of information sufficient to precisely define the positions of objects (and therefore people) around the robot, providing objective and quantitative data that can be very useful for a wide range of tasks, in particular, to perform autonomous tasks of following people. This paper uses the estimated distance from a person to a service robot to predict the behavior of a person, and thus improve performance in autonomous person following tasks. For this, we use an adaptive fuzzy neural network (AFNN) which includes a fuzzy neural network based on Takagi-Sugeno fuzzy inference, and an adaptive learning algorithm to update the membership functions and the rule base. The validity of the proposal is verified both by simulation and on a real prototype. The average RMSE of prediction over the 50 laboratory tests with different people acting as target object was 7.33.
Acoustic event characterization for service robot using convolutional networksIJECEIAES
This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcscpconf
This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.
Robot operating system based autonomous navigation platform with human robot ...TELKOMNIKA JOURNAL
In emerging technologies, indoor service robots are playing a vital role for people who are physically challenged and visually impaired. The service robots are efficient and beneficial for people to overcome the challenges faced during their regular chores. This paper proposes the implementation of autonomous navigation platforms with human-robot interaction which can be used in service robots to avoid the difficulties faced in daily activities. We used the robot operating system (ROS) framework for the implementation of algorithms used in auto navigation, speech processing and recognition, and object detection and recognition. A suitable robot model was designed and tested in the Gazebo environment to evaluate the algorithms. The confusion matrix that was created from 125 different cases points to the decent correctness of the model.
Obstacle detection for autonomous systems using stereoscopic images and bacte...IJECEIAES
This paper presents a low cost strategy for real-time estimation of the position of ob- stacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcsandit
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
This document summarizes a student research paper on the design and development of a human following robot. The robot is designed to track and follow a person wearing a custom-made tag. Key components include a camera for visual processing to detect the tag, ultrasonic sensors for obstacle avoidance and maintaining distance, and a magnetometer and encoders for navigation. The system architecture involves separate processing and control units that communicate via serial communication. Various experiments were conducted to test tag detection, distance maintenance, and navigation. Adjustments were made to algorithms and sensor placements to improve performance.
APPLICATION OF VARIOUS DEEP LEARNING MODELS FOR AUTOMATIC TRAFFIC VIOLATION D...ijitcs
A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on
road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination
faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules
puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the
rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome
this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to
detect traffic violations in real-time using edge computing, which reduces the time to detect. Different
machine learning models and algorithms were applied to detect traffic violations like traveling without a
helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented
gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson
Nano, as the fps is quite low.
Social Service Robot using Gesture recognition techniqueChristo Ananth
A robot is a machine that can automatically do a task or a series of tasks based on its programming and environment. They are artificially built machines or devices that can perform activities with utmost accuracy and precision minimizing time constraints. Service robots are technologically advanced machines deployed to service and maintain certain activities. Research findings convey the essential fact that serving robots are now being deployed worldwide. Social robotics is one such field that heavily involves an interaction between humans and an artificially built machine. These man-built machines interact with humans and can also understand social terms and words. Modernization has bought changes in design and mechanisms due to this ever-lasting growth in technology and innovation. Therefore, food industries are also dynamically adapting to the new changes in the field of automation to reduce human workload and increase the quality of service. Deployment of a robot in the food industries which help to aid deaf and mute people who face social constraints is an evergrowing challenge faced by engineers for the last few decades. Moreover, a contactless form of speedy service system which accomplishes its task with at most precision and reduced complexity is a feat yet to be perfected. Preservation of personal hygiene, a better quality of service, and reduced labour costs is achieved.
Emergence of Industry 4.0 in current economic trend promotes the usage of Internet of Things (IoT) in product development. Counting people on streets or at entrances of places is indeed beneficial for security, tracking and marketing purposes. The usage of cameras or closed-circuit television (CCTV) for surveillance purposes has emerged the need of tools for the digital imagery content analysis to improve the system. The purpose of this project is to design a cloud-based people counter using Raspberry Pi embedded system and send the received data to ThingSpeak, IoT platform. The initial stage of the project is simulation and coding development using OpenCV and Python. For the hardware development, a Pi camera is used to capture the video footage and monitor the people movement. Raspberry Pi acts as the microcontroller for the system and process the video to perform people counting. Experiment have been conducted to measure the performance of the system in the actual environment, people counting on saved video footage and visualized the data on ThingSpeak platform.
The document summarizes several papers related to crime detection using computer vision techniques. It discusses approaches for detecting fights in videos using features like STIP and MoSIFT descriptors. It also reviews methods for detecting emotions from body movements and recognizing crowd behaviors in video sequences. Several algorithms are presented, including FSCB for real-time crowd behavior detection and a three-pronged approach using texture, color, and motion history for moving object detection. The document analyzes trajectory-based and pixel-based techniques for unsupervised abnormal event detection.
Interactive full body motion capture using infrared sensor networkijcga
Traditional motion capture (mocap) has been
well
-
stud
ied in visual science for
the last decades
. However
the fie
ld is mostly about capturing
precise animation to be used in
specific
application
s
after
intensive
post
processing such as studying biomechanics or rigging models in movies. These data set
s are normally
captured in complex laboratory environments with
sophisticated
equipment thus making motion capture a
field that is mostly exclusive to professional animators.
In
addition
, obtrusive sensors must be attached to
actors and calibrated within t
he capturing system, resulting in limited and unnatural motion.
In recent year
the rise of computer vision and interactive entertainment opened the gate for a different type of motion
capture which focuses on producing
optical
marker
less
or mechanical sens
orless
motion capture.
Furtherm
ore a wide array of low
-
cost
device are released that are easy to use
for less mission critical
applications
.
This paper
describe
s
a new technique of using multiple infrared devices to process data from
multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap
using commodity
devices such as Kinect
. The method involves analyzing each individual sensor
data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from
sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasize
s on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability
Efficient and secure real-time mobile robots cooperation using visual servoing IJECEIAES
This paper deals with the challenging problem of navigation in formation of mobiles robots fleet. For that purpose, a secure approach is used based on visual servoing to control velocities (linear and angular) of the multiple robots. To construct our system, we develop the interaction matrix which combines the moments in the image with robots velocities and we estimate the depth between each robot and the targeted object. This is done without any communication between the robots which eliminate the problem of the influence of each robot errors on the whole. For a successful visual servoing, we propose a powerful mechanism to execute safely the robots navigation, exploiting a robot accident reporting system using raspberry Pi3. This reporting system testbed is used to send an accident notification, in the form of a specifical message. Experimental results are presented using nonholonomic mobiles robots with on-board real time cameras, to show the effectiveness of the proposed method.
In tech vision-based_obstacle_detection_module_for_a_wheeled_mobile_robotSudhakar Spartan
This document describes a vision-based obstacle detection module for a wheeled mobile robot. It uses a stereoscopic vision system to detect obstacles and build and update a map of the environment. The system extracts the ground surface from images using hue and detects obstacle edges using luminance. It then performs stereo matching on corresponding obstacle edge points to calculate depth and detect obstacles. The module is implemented using an FPGA to achieve high-performance real-time obstacle detection for safe robot navigation and dynamic map updating.
Cognitive Robotics: Merging AI and Robotics for Complex TasksIRJET Journal
The document discusses cognitive robotics, which merges artificial intelligence and robotics to enable robots to perform complex tasks. It describes key components of cognitive robotics like perception, reasoning, learning, interaction and autonomy. It also provides examples of data augmentation techniques to address challenges of limited labeled data when fine-tuning large language models for low-resource languages. Case studies show real-world applications of cognitive robotics in areas like logistics warehouses, surgery robots and home vacuums. The convergence of robotics and AI is advancing technologies to tackle intricate problems.
Interactive Full-Body Motion Capture Using Infrared Sensor Network ijcga
The document describes a new technique for interactive full-body motion capture using multiple infrared sensors. It processes data from each sensor independently and then combines the results to enhance flexibility and accuracy. The method aims to maintain real-time performance while improving on issues like limited actor orientation, inaccurate joint tracking, and conflicting data from individual sensors.
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm IJECEIAES
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments.
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
The document describes a proposed system called SRAVIP (Smart Robot Assistant for Visually Impaired Persons) that aims to assist visually impaired individuals in navigating indoor environments. SRAVIP includes two subsystems: 1) an initialization system to create an environment map and register users, and 2) a real-time operation system to navigate the mobile robot and communicate with users through speech or text. The robot utilizes sensors and simultaneous localization and mapping to safely guide a registered user to their desired location indoors. The system was tested successfully using a Turtlebot3 robot at a university campus.
Research on object detection and recognition using machine learning algorithm...YousefElbayomi
This document discusses research on using machine learning and deep learning for object detection. It examines applications in autonomous vehicles, image detection for agriculture, and credit card fraud detection. For autonomous vehicles, deep learning is discussed for object identification and perception, though speed and real-world performance need improvement. Image detection for agriculture uses feature extraction and machine learning for automatic fruit identification. Credit card fraud detection uses ensemble methods like LightGBM, XGBoost and CatBoost on preprocessed transaction data to identify fraudulent transactions. The document evaluates different approaches and their challenges for these applications of object detection.
This document describes a proposed sign language interpreter system that uses machine learning and computer vision techniques. It aims to enable deaf and mute users to communicate through computers and the internet by recognizing static hand gestures from camera input and translating them to text. The proposed system extracts features from captured images of signs and uses a support vector machine model to classify the gestures by comparing to a dataset of labeled images. If implemented, this system could help overcome communication barriers for deaf users in an increasingly digital world.
IRJET - An Intelligent Pothole Detection System using Deep LearningIRJET Journal
This document describes a proposed intelligent pothole detection system using deep learning. The system would use a convolutional neural network trained on pothole image data to detect potholes in photos taken from a vehicle mounted camera. When potholes are detected, their locations would be stored in a cloud database. A mobile app would allow users to view the locations of detected potholes on a map. This would help automate pothole detection to assist road maintenance authorities and provide drivers with pothole location information. The proposed system aims to address the inefficiencies of manual pothole detection by automating the process using deep learning and cloud/mobile technologies.
Visual victim detection and quadrotor-swarm coordination control in search an...IJECEIAES
We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Finally, images are processed using CNN identifying potential victims in the area. Given the uncertainty of the victim detection measurement among quadrotors’ cameras in the image processing, estimation consensus (EC) and max-estimation consensus (M-EC) algorithms are proposed focusing on agreeing over the victim detection estimation. We illustrate that M-EC delivers better results than EC in scenarios with poor visibility and uncertainty produced by fire and smoke. The algorithm proves that distributed fashion can obtain a more accurate result in decision-making on whether or not there is a victim, showing robustness under uncertainties and wrong measurements in comparison when a single quadrotor performs the mission. The well-functioning of the algorithm is evaluated by carrying out a simulation using V-Rep.
Arduino Based Hand Gesture Controlled RobotIRJET Journal
This document describes an Arduino-based hand gesture controlled robot. The robot uses image processing of hand gestures captured by a camera to determine commands. The hand gestures are compared to reference images to detect gestures like moving up, down, left, right. The corresponding command is sent wirelessly to the robot via ZigBee. The robot contains an Arduino microcontroller, accelerometer, motor driver and receives signals through a Zigbee receiver. Based on the command signal, the microcontroller controls the motor polarity to move the robot in the appropriate direction, allowing it to be controlled remotely through hand gestures without any physical controls. This provides a natural human-machine interface for applications like industrial and medical robotics.
The document discusses gesture-based computing as an alternative to mouse input for human-computer interaction. It proposes a novel approach for implementing a real-time gesture recognition system capable of understanding commands based on analyzing the principal contour and fingertips of hand gestures. Vision-based gesture recognition techniques are discussed that do not require additional devices for users to interact with computers through natural hand motions.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Weitere ähnliche Inhalte
Ähnlich wie A novel visual tracking scheme for unstructured indoor environments
This document summarizes a student research paper on the design and development of a human following robot. The robot is designed to track and follow a person wearing a custom-made tag. Key components include a camera for visual processing to detect the tag, ultrasonic sensors for obstacle avoidance and maintaining distance, and a magnetometer and encoders for navigation. The system architecture involves separate processing and control units that communicate via serial communication. Various experiments were conducted to test tag detection, distance maintenance, and navigation. Adjustments were made to algorithms and sensor placements to improve performance.
APPLICATION OF VARIOUS DEEP LEARNING MODELS FOR AUTOMATIC TRAFFIC VIOLATION D...ijitcs
A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on
road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination
faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules
puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the
rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome
this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to
detect traffic violations in real-time using edge computing, which reduces the time to detect. Different
machine learning models and algorithms were applied to detect traffic violations like traveling without a
helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented
gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson
Nano, as the fps is quite low.
Social Service Robot using Gesture recognition techniqueChristo Ananth
A robot is a machine that can automatically do a task or a series of tasks based on its programming and environment. They are artificially built machines or devices that can perform activities with utmost accuracy and precision minimizing time constraints. Service robots are technologically advanced machines deployed to service and maintain certain activities. Research findings convey the essential fact that serving robots are now being deployed worldwide. Social robotics is one such field that heavily involves an interaction between humans and an artificially built machine. These man-built machines interact with humans and can also understand social terms and words. Modernization has bought changes in design and mechanisms due to this ever-lasting growth in technology and innovation. Therefore, food industries are also dynamically adapting to the new changes in the field of automation to reduce human workload and increase the quality of service. Deployment of a robot in the food industries which help to aid deaf and mute people who face social constraints is an evergrowing challenge faced by engineers for the last few decades. Moreover, a contactless form of speedy service system which accomplishes its task with at most precision and reduced complexity is a feat yet to be perfected. Preservation of personal hygiene, a better quality of service, and reduced labour costs is achieved.
Emergence of Industry 4.0 in current economic trend promotes the usage of Internet of Things (IoT) in product development. Counting people on streets or at entrances of places is indeed beneficial for security, tracking and marketing purposes. The usage of cameras or closed-circuit television (CCTV) for surveillance purposes has emerged the need of tools for the digital imagery content analysis to improve the system. The purpose of this project is to design a cloud-based people counter using Raspberry Pi embedded system and send the received data to ThingSpeak, IoT platform. The initial stage of the project is simulation and coding development using OpenCV and Python. For the hardware development, a Pi camera is used to capture the video footage and monitor the people movement. Raspberry Pi acts as the microcontroller for the system and process the video to perform people counting. Experiment have been conducted to measure the performance of the system in the actual environment, people counting on saved video footage and visualized the data on ThingSpeak platform.
The document summarizes several papers related to crime detection using computer vision techniques. It discusses approaches for detecting fights in videos using features like STIP and MoSIFT descriptors. It also reviews methods for detecting emotions from body movements and recognizing crowd behaviors in video sequences. Several algorithms are presented, including FSCB for real-time crowd behavior detection and a three-pronged approach using texture, color, and motion history for moving object detection. The document analyzes trajectory-based and pixel-based techniques for unsupervised abnormal event detection.
Interactive full body motion capture using infrared sensor networkijcga
Traditional motion capture (mocap) has been
well
-
stud
ied in visual science for
the last decades
. However
the fie
ld is mostly about capturing
precise animation to be used in
specific
application
s
after
intensive
post
processing such as studying biomechanics or rigging models in movies. These data set
s are normally
captured in complex laboratory environments with
sophisticated
equipment thus making motion capture a
field that is mostly exclusive to professional animators.
In
addition
, obtrusive sensors must be attached to
actors and calibrated within t
he capturing system, resulting in limited and unnatural motion.
In recent year
the rise of computer vision and interactive entertainment opened the gate for a different type of motion
capture which focuses on producing
optical
marker
less
or mechanical sens
orless
motion capture.
Furtherm
ore a wide array of low
-
cost
device are released that are easy to use
for less mission critical
applications
.
This paper
describe
s
a new technique of using multiple infrared devices to process data from
multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap
using commodity
devices such as Kinect
. The method involves analyzing each individual sensor
data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from
sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasize
s on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability
Efficient and secure real-time mobile robots cooperation using visual servoing IJECEIAES
This paper deals with the challenging problem of navigation in formation of mobiles robots fleet. For that purpose, a secure approach is used based on visual servoing to control velocities (linear and angular) of the multiple robots. To construct our system, we develop the interaction matrix which combines the moments in the image with robots velocities and we estimate the depth between each robot and the targeted object. This is done without any communication between the robots which eliminate the problem of the influence of each robot errors on the whole. For a successful visual servoing, we propose a powerful mechanism to execute safely the robots navigation, exploiting a robot accident reporting system using raspberry Pi3. This reporting system testbed is used to send an accident notification, in the form of a specifical message. Experimental results are presented using nonholonomic mobiles robots with on-board real time cameras, to show the effectiveness of the proposed method.
In tech vision-based_obstacle_detection_module_for_a_wheeled_mobile_robotSudhakar Spartan
This document describes a vision-based obstacle detection module for a wheeled mobile robot. It uses a stereoscopic vision system to detect obstacles and build and update a map of the environment. The system extracts the ground surface from images using hue and detects obstacle edges using luminance. It then performs stereo matching on corresponding obstacle edge points to calculate depth and detect obstacles. The module is implemented using an FPGA to achieve high-performance real-time obstacle detection for safe robot navigation and dynamic map updating.
Cognitive Robotics: Merging AI and Robotics for Complex TasksIRJET Journal
The document discusses cognitive robotics, which merges artificial intelligence and robotics to enable robots to perform complex tasks. It describes key components of cognitive robotics like perception, reasoning, learning, interaction and autonomy. It also provides examples of data augmentation techniques to address challenges of limited labeled data when fine-tuning large language models for low-resource languages. Case studies show real-world applications of cognitive robotics in areas like logistics warehouses, surgery robots and home vacuums. The convergence of robotics and AI is advancing technologies to tackle intricate problems.
Interactive Full-Body Motion Capture Using Infrared Sensor Network ijcga
The document describes a new technique for interactive full-body motion capture using multiple infrared sensors. It processes data from each sensor independently and then combines the results to enhance flexibility and accuracy. The method aims to maintain real-time performance while improving on issues like limited actor orientation, inaccurate joint tracking, and conflicting data from individual sensors.
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm IJECEIAES
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments.
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
The document describes a proposed system called SRAVIP (Smart Robot Assistant for Visually Impaired Persons) that aims to assist visually impaired individuals in navigating indoor environments. SRAVIP includes two subsystems: 1) an initialization system to create an environment map and register users, and 2) a real-time operation system to navigate the mobile robot and communicate with users through speech or text. The robot utilizes sensors and simultaneous localization and mapping to safely guide a registered user to their desired location indoors. The system was tested successfully using a Turtlebot3 robot at a university campus.
Research on object detection and recognition using machine learning algorithm...YousefElbayomi
This document discusses research on using machine learning and deep learning for object detection. It examines applications in autonomous vehicles, image detection for agriculture, and credit card fraud detection. For autonomous vehicles, deep learning is discussed for object identification and perception, though speed and real-world performance need improvement. Image detection for agriculture uses feature extraction and machine learning for automatic fruit identification. Credit card fraud detection uses ensemble methods like LightGBM, XGBoost and CatBoost on preprocessed transaction data to identify fraudulent transactions. The document evaluates different approaches and their challenges for these applications of object detection.
This document describes a proposed sign language interpreter system that uses machine learning and computer vision techniques. It aims to enable deaf and mute users to communicate through computers and the internet by recognizing static hand gestures from camera input and translating them to text. The proposed system extracts features from captured images of signs and uses a support vector machine model to classify the gestures by comparing to a dataset of labeled images. If implemented, this system could help overcome communication barriers for deaf users in an increasingly digital world.
IRJET - An Intelligent Pothole Detection System using Deep LearningIRJET Journal
This document describes a proposed intelligent pothole detection system using deep learning. The system would use a convolutional neural network trained on pothole image data to detect potholes in photos taken from a vehicle mounted camera. When potholes are detected, their locations would be stored in a cloud database. A mobile app would allow users to view the locations of detected potholes on a map. This would help automate pothole detection to assist road maintenance authorities and provide drivers with pothole location information. The proposed system aims to address the inefficiencies of manual pothole detection by automating the process using deep learning and cloud/mobile technologies.
Visual victim detection and quadrotor-swarm coordination control in search an...IJECEIAES
We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Finally, images are processed using CNN identifying potential victims in the area. Given the uncertainty of the victim detection measurement among quadrotors’ cameras in the image processing, estimation consensus (EC) and max-estimation consensus (M-EC) algorithms are proposed focusing on agreeing over the victim detection estimation. We illustrate that M-EC delivers better results than EC in scenarios with poor visibility and uncertainty produced by fire and smoke. The algorithm proves that distributed fashion can obtain a more accurate result in decision-making on whether or not there is a victim, showing robustness under uncertainties and wrong measurements in comparison when a single quadrotor performs the mission. The well-functioning of the algorithm is evaluated by carrying out a simulation using V-Rep.
Arduino Based Hand Gesture Controlled RobotIRJET Journal
This document describes an Arduino-based hand gesture controlled robot. The robot uses image processing of hand gestures captured by a camera to determine commands. The hand gestures are compared to reference images to detect gestures like moving up, down, left, right. The corresponding command is sent wirelessly to the robot via ZigBee. The robot contains an Arduino microcontroller, accelerometer, motor driver and receives signals through a Zigbee receiver. Based on the command signal, the microcontroller controls the motor polarity to move the robot in the appropriate direction, allowing it to be controlled remotely through hand gestures without any physical controls. This provides a natural human-machine interface for applications like industrial and medical robotics.
The document discusses gesture-based computing as an alternative to mouse input for human-computer interaction. It proposes a novel approach for implementing a real-time gesture recognition system capable of understanding commands based on analyzing the principal contour and fingertips of hand gestures. Vision-based gesture recognition techniques are discussed that do not require additional devices for users to interact with computers through natural hand motions.
Ähnlich wie A novel visual tracking scheme for unstructured indoor environments (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
A novel visual tracking scheme for unstructured indoor environments
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6216∼6227
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6216-6227 ❒ 6216
A novel visual tracking scheme for unstructured indoor
environments
Fredy Martı́nez, Holman Montiel, Fernando Martı́nez
Facultad Tecnológica, Universidad Distrital Francisco José de Caldas, Bogotá D.C, Colombia
Article Info
Article history:
Received Mar 27, 2023
Revised Jun 8, 2023
Accepted Jun 22, 2023
Keywords:
Differential filter
Indoor environment
Interaction
K-means clustering
Real-time
Visual tracking
ABSTRACT
In the ever-expanding sphere of assistive robotics, the pressing need for ad-
vanced methods capable of accurately tracking individuals within unstructured
indoor settings has been magnified. This research endeavours to devise a real-
time visual tracking mechanism that encapsulates high performance attributes
while maintaining minimal computational requirements. Inspired by the neu-
ral processes of the human brain’s visual information handling, our innovative
algorithm employs a pattern image, serving as an ephemeral memory, which fa-
cilitates the identification of motion within images. This tracking paradigm was
subjected to rigorous testing on a Nao humanoid robot, demonstrating notewor-
thy outcomes in controlled laboratory conditions. The algorithm exhibited a re-
markably low false detection rate, less than 4%, and target losses were recorded
in merely 12% of instances, thus attesting to its successful operation. More-
over, the algorithm’s capacity to accurately estimate the direct distance to the
target further substantiated its high efficacy. These compelling findings serve
as a substantial contribution to assistive robotics. The proficient visual tracking
methodology proposed herein holds the potential to markedly amplify the com-
petencies of robots operating in dynamic, unstructured indoor settings, and set
the foundation for a higher degree of complex interactive tasks.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Fredy Martı́nez
Facultad Tecnológica, Universidad Distrital Francisco José de Caldas
Carrera 7 No 40B-53, Bogotá D.C., Colombia
Email: fhmartinezs@udistrital.edu.co
1. INTRODUCTION
The integration of robots into everyday human life is a rapidly developing field that presents numerous
research challenges and opportunities [1]. Assistive robotics, which is the use of robots in homes to provide
care, entertainment, and company, has gained significant attention in recent years [2]. This type of applica-
tion requires the development of advanced robotics technologies, such as control, learning, bipedal walking,
fine manipulation, and human-robot interaction [3]. However, the use of robots in unstructured and dynamic
environments, designed for human interaction, presents new and complex challenges. For assistive robots to
be effective, they must be able to understand the intentions of human beings by sensing their behavior in the
environment. This task requires the integration of multiple technologies and approaches, including computer
vision, machine learning, and human-robot interaction. Despite the progress that has been made in these areas,
many of the proposed solutions are still computationally demanding and require further refinement [4]. In or-
der to overcome these challenges and fully realize the potential of assistive robotics, it is essential to conduct
rigorous and interdisciplinary research that advances the state-of-the-art in control, learning, bipedal walking,
Journal homepage: http://ijece.iaescore.com
2. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 6217
fine manipulation, and human-robot interaction.
As the field of robotics continues to evolve, the integration of robots into daily human life has become a
central focus for researchers. Assistive robots are designed to perform tasks such as surveillance, entertainment,
companionship, and care, which require them to interact with humans in unstructured, dynamic environments
[5]. These robots present new challenges, as they must recognize people, track them, and understand their state
of mind and activity [6], [7]. The foundation of social interaction is visual information, which is why robots
that interact with humans must process visual information in a way that imitates the human brain [8], [9]. The
processing of visual information by the human brain is a complex process that requires the ability to recognize
and track people in real-time [10]. The challenge of determining the position of a human body in a video stream
is significant due to the many variations of the body throughout the sequence [11].
In recent literature, there have been numerous applications of tracking and control from camera im-
ages, including autonomous applications in real-time, where the robot extracts information from the image to
make motion decisions [12]. These applications rely on specific hardware configurations and algorithms that
have been developed to support them. However, the ability to process visual information in a way that mimics
the human brain and operates with a low computational cost remains an open engineering problem [13], [14].
The robotics has made significant progress in recent years, with advancements in areas such as visual percep-
tion and tracking. For this purpose, various techniques have been proposed to capture the visual information
and process it to identify and track people. One such technique is pattern recognition, which looks for specific
shapes, colors, and movement characteristics in the images captured by the robot’s cameras [15]. This process
is complex, as the robot must identify the correct set of parameters to characterize a person, which can be
challenging given the diversity of operating environments [16]. Moreover, some techniques, such as the use of
histograms of oriented gradient (HOG) descriptors and support vector machine classifiers, are computationally
expensive for real-time applications on small autonomous robots [17].
The robot must know the pattern beforehand to use pattern recognition effectively. However, this is
not always possible, especially in a service robot, which must operate in a variety of environments and interact
with a diverse set of people. In these cases, it is better for the robot to autonomously learn the pattern through
its interactions with people [18]. For example, the robot could learn to recognize a person’s silhouette through
observing their movements. This approach is particularly important when the pattern (in this case, the silhouette
of a person) is not rigid, but instead changes with their movements. Thus, learning the pattern should focus
more on movement observation than on the detection of people in images.
The majority of visual tracking systems only employ monocular vision, which is prone to high false
detection rates due to the absence of depth information in two-dimensional images [19]. To overcome this
limitation, stereoscopic vision has been proposed as a solution, which utilizes two cameras to provide depth
information, leading to a reduction in the number of false detections [20]. However, the use of stereoscopic
vision comes with an increased computational cost, which may restrict its use in smaller platforms.
To further improve the performance of visual tracking systems, researchers have proposed combining
the camera system with other sensors, such as acoustic sensors or laser range finders (LRF). This strategy allows
for the integration of additional information, increasing the robustness of the system. However, this approach
also increases the computational load and the complexity of the algorithms involved, making it challenging
to implement in small platforms [15], [21]. Additionally, the use of people as tracking elements may not be
feasible in service robots, which need to operate in a variety of environments without specific cues.
In this research, we propose a novel identification and tracking scheme for human beings based on
their movement in video frames. Our scheme operates under the assumption that the movement in the frames
is caused by a human. The scheme involves three main steps. First, the movement is detected by a differential
filter, which has a memory effect on the robot and detects combined patterns of moving objects and colors
in the frames. Second, the information gathered from the differential filter is then classified using a k-means
clustering algorithm. The k-means clustering algorithm is used to determine the presence and position of
the human, information that is then used by the robot to react and track the person. The algorithm has been
implemented directly in Python, without relying on image processing libraries such as OpenCV, to reduce its
computational requirement and allow for fast execution in parallel with other tasks. Finally, the algorithm has
been implemented on the Nao robot developed by Aldebaran Robotics (SoftBank Group), however, its design is
scalable and can be implemented on small robots as well [22]. The scheme can also be augmented with auditory
information through acoustic tracking [23], either as parallel schemes or by integrating the two sensors into a
single algorithm [24]. The integration of auditory information with visual information can provide a more
A novel visual tracking scheme for unstructured indoor environments (Fredy Martı́nez)
3. 6218 ❒ ISSN: 2088-8708
robust tracking solution, which is especially useful in noisy or cluttered environments.
2. BACKGROUND
In recent years, there has been a growing interest in the development of assistive navigation systems
and mobile robots to aid blind and visually impaired individuals with indoor travel and to support human-robot
interaction experiments. A number of studies have explored different approaches to solving the challenges
associated with these systems. For instance, [25] presented a novel vision-based mobile assistive navigation
system that helps blind and visually impaired individuals to travel independently indoors. The system integrates
various sensors and algorithms to provide a holistic solution that enables these individuals to navigate and
avoid obstacles in their environment. In [26] introduced the Georgia Tech Miniature Autonomous Blimp (GT-
MAB), which is designed to support human-robot interaction experiments in indoor spaces. The GT-MAB is
equipped with sensors and algorithms that allow it to interact with humans and respond to their movements.
This system provides a unique platform for studying human-robot interaction and exploring new approaches to
assistive navigation. In [27] focused on an alternative solution to existing filtering techniques by introducing the
belief condensation filter (BCF) for localization via Bluetooth low energy (BLE)-enabled beacons. The BCF
is designed to provide a more efficient and effective way to determine the location of a mobile robot within
an environment. By incorporating the BCF, the researchers were able to demonstrate improved accuracy and
robustness in the localization of mobile robots.
In another study, Feng et al. [28] proposed an integrated indoor positioning system (IPS) that com-
bines the use of inertial measurement units (IMU) and ultra-wideband (UWB) technology. By integrating these
technologies, the researchers were able to improve the robustness and accuracy of the IPS by using the extended
Kalman filter (EKF) and unscented Kalman filter (UKF). The IPS provides a comprehensive solution for deter-
mining the location of a mobile robot within an indoor environment. Al Khatib et al. [29] presented a low-cost
approach for solving the navigation problem of wheeled mobile robots in indoor and outdoor environments.
The researchers proposed an efficient geometric outlier detection method that uses dynamic information from
previous frames and a novel probability model to judge moving objects, with the help of geometric constraints
and human detection [30]. Liu and Miura [31] proposed a fuzzy detection strategy to prejudge the tracking
result and to improve the accuracy of the navigation system. Additionally, they proposed an efficient geometric
outlier detection method that uses dynamic information from previous frames and a novel probability model to
judge moving objects with the help of geometric constraints and human detection. Xue et al. [32] explored the
tracking problem of cluster targets and proposed new solutions to improve the accuracy of target tracking.
The recent trend towards the development of internet of things (IoT) architectures has led to the trans-
formation of standard camera networks into smart multi-device systems that are capable of acquiring, elaborat-
ing, and exchanging data, and adapting to the environment dynamically. Giordano et al. [33] proposed a novel
distributed solution that guarantees real-time monitoring of 3D indoor structured areas and tracking of multiple
targets. The solution employs a heterogeneous visual sensor network composed of both fixed and pan-tilt-zoom
(PTZ) cameras. Finally, Shi et al. [34] proposed a novel hybrid method that combines visual and probabilistic
localization results to improve the accuracy of indoor positioning systems. By combining these two techniques,
the researchers were able to demonstrate improved accuracy in the determination of the location of a mobile
robot within an indoor environment.
In conclusion, the field of robotics has seen significant advancements in the development of solutions
to support the navigation and localization needs of mobile robots operating in indoor environments. A range
of approaches have been proposed, including vision-based systems, integration of IMU and UWB, low-cost
approaches, and the incorporation of probabilistic methods. The increasing trend towards the development of
IoT architectures has led to the transformation of standard camera networks into smart multi-device systems
capable of acquiring, elaborating and exchanging data, and adapting dynamically to the environment. In this
line, novel solutions have been proposed that guarantee real-time monitoring and tracking of multiple targets
using heterogeneous visual sensor networks. These advancements represent significant steps forward in the
development of robust and accurate navigation systems for indoor environments and provide promising avenues
for future research and development.
3. METHOD
The visual tracking scheme presented in this study is designed to achieve high performance while be-
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6216-6227
4. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 6219
ing being simple and computationally efficient. The scheme aims to minimize false detections and maintain
stability in tracking the target object. The approach utilizes monocular vision and only processes images
captured by the robot’s camera when it is not undergoing any movement. The images captured by the robot’s
camera undergo a processing stage, where the existence of movement in the environment is determined. This
is achieved through the use of image analysis algorithms and algorithms that detect changes in position and
movement patterns. The proposed scheme does not rely on additional sensors, making it a computationally
efficient solution for human tracking in robotics.
The architecture of the visual tracking scheme is illustrated in Figure 1. The figure provides a high-
level overview of the components and steps involved in the tracking algorithm. The algorithm is designed to
operate in real-time, enabling the robot to quickly respond to changes in its environment. The approach taken
in the design of the algorithm balances computational efficiency with performance, making it a valuable con-
tribution.
Figure 1. Architecture of the proposed visual tracking scheme
3.1. Differential filter
Our differential filter operates based on the principles of how the human brain processes visual infor-
mation from the eyes. Just like the retina in the eye, which is located at the back and receives light through
specialized cells (rods and cones), our filter differentiates between peripheral and central information. The rods
in the eye, responsible for peripheral vision, have high sensitivity to light but are unable to detect details, while
the cones, located mostly in the center of the retina, are capable of detecting details and colors. This means
that despite having a wide peripheral vision, the eye only processes detailed information in the center of vision
where the gaze is focused [35]. As the center of focus moves away, the level of detail decreases.
Our differential filter mimics this biological strategy by focusing its attention on the object of interest,
in this case a moving object (human body), and disregarding irrelevant information [36]. The filter detects
the object by comparing the current image with a pattern image stored in memory that is constructed from
the changes in the pixel values of previous images. This process is a representation of the brain’s autonomous
mechanism for processing unknown information by relating it to patterns stored in memory based on individual
experience, reducing the amount of information to be processed and the total processing time.
The human brain’s ability to recognize patterns and relate new information to previously stored infor-
mation is a crucial aspect of visual processing. This process of relating new information to stored patterns is a
powerful strategy for reducing the amount of information that needs to be processed and stored, thus optimizing
A novel visual tracking scheme for unstructured indoor environments (Fredy Martı́nez)
5. 6220 ❒ ISSN: 2088-8708
the processing time and storage costs. The mechanism behind this is autonomous and enables individuals to
work with unknown information by relating it to the stored memories. This biological strategy is what makes
people identify figures in clouds or faces on toast. The brain continually tries to complete the visual information
received from the eyes with previous information stored in memory. By comparing the incoming information
with patterns already stored, the brain can quickly make connections and categorize the information, thereby
reducing the amount of time and resources required for processing.
Moreover, the brain performs a series of information selection processes to discard irrelevant informa-
tion, such as information that is not directly related to the task at hand. This selection process further optimizes
the visual processing system by reducing the amount of data that needs to be processed, increasing the speed
and accuracy of the process. In this way, the functioning of the human brain when processing visual informa-
tion from the eyes can be seen as a highly optimized system that balances the need for detail and speed, and
ensures the most efficient use of resources. This mechanism of processing visual information serves as a model
for our differential filter, which mimics the functioning of the human brain to optimize the processing of visual
information in our robotic system.
The operating model of our system incorporates the essential features of the human brain’s visual
information processing mechanism. Detection and tracking of objects in motion is achieved through the identi-
fication of movements, with the moving object, in this case, the human body, serving as the center of attention.
The rest of the visual information is categorized as background and is considered irrelevant. In this approach,
the visual information is differentiated into two main components: the object and the background. The object
is the focus of attention, the item in motion that is being monitored, while the background encompasses all the
surrounding elements. This differentiation between the object and background is crucial in enabling the system
to efficiently process and analyze the visual information, allowing it to selectively focus its attention on the
moving object.
By replicating the human brain’s visual information processing mechanism, our system is able to
achieve a high level of accuracy and efficiency in detecting and tracking objects in motion, making it a valuable
tool in various fields, including but not limited to, robotics, surveillance, and human-computer interaction. The
object (human body) is detected by comparing the current image CI(t) with another pattern image stored in
memory BI(t) (background image). This pattern image is constructed from the pixel changes of the previous
images. The value of each pixel is updated with each image captured according to (1):
gi = βigi + (1 − βi) × p (CI (t))i ⧸i = 1, 2, . . . , l (1)
where:
− l is the number of pixels in the image, i.e. l = m × n for an image of m × n pixels.
− pi is the grayscale color (amount of light in the pixel) of the i-th pixel in the image.
− bi is the distance between the pixels i of the images BI(t) and BI(t-1) calculated as (2):
βi = 1 −
|p (BI (t))i − p (BI (t − 1))i|
255
(2)
This β inherently exhibits an inverse relationship with pixel distances, ranging from 0 to 1, allocating
high values when pixel distances are compact, and conversely, low values when these distances are expansive.
One of the defining features of our filter is its ability to create and systematically update a pattern image, denoted
by BI (t), which is temporarily archived in memory. This pattern image formation revolves around the vigilant
tracking of fluctuations in pixel values within a sequence of captured images over a specified timeframe, as
mathematically represented in (1).
The functional essence of this pattern image resides in its role as a point of reference for identifying
moving entities in the present image, termed as CI (t). This is achieved through a comparative analysis between
the pattern image and the current image. Given the continuous updating mechanism of the pattern image, it
possesses the capacity to rapidly register alterations and discern between elements that exhibit motion and sta-
tionary background elements. Interestingly, background entities that cease motion are eventually disregarded
in this process. Figure 2 provides a graphical illustration encapsulating the operational behavior of our differ-
ential filter. Furthermore, the practical application and overall functionality of our differential filter is vividly
demonstrated in Figure 3.
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6216-6227
6. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 6221
Figure 2. Pattern image construction from in-memory images
Figure 3. Differential filter operation when a person appears in front of the robot
3.2. Position identifier
The k-means clustering algorithm is a widely used unsupervised machine learning technique that aims
to partition a given dataset into a predefined number of clusters, k, based on the similarity of their features. The
algorithm operates by iteratively updating the centroids of the clusters until convergence, where the data points
are assigned to the nearest centroid. One of the key strengths of k-means is its simplicity and computational
efficiency, making it a popular choice for many real-world applications such as image segmentation, text clus-
tering, and customer segmentation. Despite its simplicity, k-means is a powerful tool that can effectively
identify meaningful patterns and structures in large and complex datasets.
To effectively apply k-means, it is essential to choose an appropriate value for k. In general, a larger
value of k can lead to finer grained clusters, but may also result in over-fitting the data. On the other hand,
a smaller value of k may result in coarser clusters that do not accurately reflect the underlying structure of
the data. A popular heuristic for choosing k is the elbow method, which involves analyzing the relationship
between the number of clusters and the within-cluster sum of squares.
Once k has been selected, k-means operates by first initializing the centroids randomly, and then iter-
atively updating the cluster assignments and centroids. The algorithm terminates when the cluster assignments
no longer change, or when a maximum number of iterations has been reached. The final result of the k-means
algorithm is a partition of the data into k clusters, with each cluster represented by its centroid. In our study, we
use a dataset consisting of pairs of differential filter results, CR(t), and corresponding current images, CI (t), in
RGB color format. To effectively incorporate both spatial and color information in our analysis, we represent
each pixel as a vector that consists of both its position (x, y) and its color information.
However, it is important to note that the color information is only used for the pixels that are identi-
fied in the comparison result CR(t). For the background pixels in the image, which are not identified by the
A novel visual tracking scheme for unstructured indoor environments (Fredy Martı́nez)
7. 6222 ❒ ISSN: 2088-8708
differential filter, we assign a white color value of (R = 255, G = 255, B = 255). This allows us to effectively
incorporate both the result of the differential filter comparison and the color information of the current image in
our analysis. By defining the dataset in this manner, we are able to effectively capture both the spatial and color
information of the pixels in the image, which will be useful for subsequent processing and analysis. This is a
crucial step in our study, as it provides the necessary foundation for our analysis and allows us to effectively
utilize both the information provided by the differential filter and the color information of the current image.
Vectors are defined as (3).
qi = (qR.i, qG,i, qB,i, qx,i, qy,i) ⧸i = 1, 2, . . . , l (3)
In our study, we have chosen to define a total of k=3 clusters to be identified in each dataset using the k-means
algorithm. The purpose of this is to gain a deeper understanding of the data and to be able to accurately
estimate various attributes related to the presence of a person in the dataset. The k-means algorithm provides
the coordinates (x, y) of three points that, when combined, form a triangular area. This triangular area is easily
analyzed to estimate if the object within the area is indeed a person. Furthermore, we are able to use the
information provided by the k-means algorithm to estimate the approximate distance from the person to the
robot, as well as the relative position of the person’s head.
Additionally, the triangular area and the information provided by the k-means algorithm can also be
used to estimate whether the person is standing or not. This is a crucial step in our study, as it provides valuable
information that can be used to inform the robot’s actions and interactions with the person. The objective of
the robot is to attentively monitor the movements of human beings. To achieve this, we use the information
obtained from the k-means algorithm, which provides valuable insights into the presence and attributes of a
person in the dataset.
The robot has been programmed to respond to human movements, with the head being the only re-
sponse element in our laboratory tests. By utilizing the information provided by the k-means algorithm, we
are able to coordinate the movements of the robot’s head in a way that ensures that the robot always tracks the
head of the person. Based on the size and shape of the triangular area, we estimate the distance between the
robot and the person. Additionally, we are able to determine the position of the person’s head, which is critical
information for the robot to effectively track the person. In order to ensure that the robot is always attentively
monitoring the person, it is crucial that the face of the robot is always directed towards the person. This is a
critical step in our study, as it allows us to ensure that the robot is effectively able to monitor and respond to
the movements of human beings.
4. RESULT AND DISCUSSION
We utilize two distinct robotic platforms to address the challenges of human-robot interaction and
indoor navigation. The humanoid Nao robot from SoftBank Group serves as the primary interface for human
interaction, providing a natural and intuitive means for communication and collaboration with the environment
[14]. Equipped with advanced sensors and actuators, the Nao robot allows for real-time monitoring of hu-
man behavior and the ability to respond dynamically to changing scenarios. On the other hand, the ARMOS
TurtleBot 1 robot from the Arquitecturas Modernas para Sistemas de Alimentación (ARMOS) research group
was employed for indoor navigation, leveraging its superior mobility and navigation capabilities. The Turtle-
Bot robot was integrated with the Nao robot through a Wi-Fi connection, providing seamless communication
between the two platforms as seen in Figure 4.
To implement our proposed solution, we developed the algorithm in Python, which was programmed
onto the Nao robot. The use of Python allowed us to effectively harness the full power of the Nao robot’s
hardware and software capabilities, while also providing a highly accessible and scalable platform for future
developments and advancements. The combination of these two robotic platforms, along with the implemen-
tation of our novel algorithm, has the potential to deliver significant improvements in the field of assistive
robotics, opening up new avenues for innovative applications and research [29].
We utilized high resolution images of size 1280×720 pixels to capture the movements of objects in
the environment. In order to ensure that the movements recorded in the images were solely the result of objects
external to the robot, a decision was made to only capture a single image when the robot was not making any
movements. This approach was based on the biological model of visual perception, which suggests that in the
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6216-6227
8. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 6223
absence of any movement, the image captured at a given time remains static. To further enhance the accuracy
of our results, the images were captured at an interval of 100 milliseconds when no movement was detected.
This allowed us to accurately record and process the movements of external objects in real-time, enabling the
robot to respond appropriately to its environment.
Figure 4. Experimental setup for the human-tracking robot. It is composed of a humanoid Nao robot from
SoftBank Group at the top and an ARMOS TurtleBot 1 tank robot from the ARMOS research group at the
bottom
In our experiments, we aimed to thoroughly test the capabilities of our assistive robot under various
conditions. To achieve this, we conducted a series of experiments with varying lighting conditions, environ-
ments, and distances between the robot and the people involved. This was to ensure that our robot could
effectively perform its intended tasks in various scenarios, and provide meaningful assistance to the people it
interacts with. To further validate our approach, we also performed tests with multiple people present in the
robot’s field of vision. These tests allowed us to evaluate the robot’s ability to distinguish between multiple
individuals, and track their movements accurately. This information is crucial in ensuring that our robot can
provide personalized assistance to multiple individuals simultaneously.
We documented our experiments using video footage, which can be seen in the accompanying video
link [37]. This footage provides visual evidence of the robustness and effectiveness of our assistive robot,
and highlights its ability to perform its intended tasks under different conditions. The video footage is also
accompanied by a still image see Figure 5, which provides a visual representation of one of our experiments.
Figure 5. Development of one of the visual tracking tests
A novel visual tracking scheme for unstructured indoor environments (Fredy Martı́nez)
9. 6224 ❒ ISSN: 2088-8708
The robot has the capability to detect movement in its environment with high accuracy. The algorithm
used to achieve this demonstrated remarkable performance in laboratory tests, surpassing the expectations of
the researchers. False detections occurred infrequently, only in 4% of the cases, and were primarily due to the
movement of the ARMOS TurtleBot 1 platform, which currently does not have direct communication with the
Nao robot. A small percentage (12%) of target losses were observed, typically when the object was moving at
high speeds and exceeded the robot’s image capture rate.
We aimed to ascertain the direct, real-world distance separating the robot and a human entity within
the same environmental context [8]. To achieve this, we employed a method centered around geometric analysis
of a triangular region distinctly defined by three distinct data points. These specific points were procured by
leveraging the statistical of the k-means clustering algorithm. The triangular area constituted by these three
points was then meticulously scrutinized. Our method extrapolated the spatial relation of the points within the
triangle, using their relative positioning and the magnitude of the area they enclosed, to draw inferences about
the actual physical distance between the robot and the individual.
Despite the simplicity of the algorithm, the results obtained were quite remarkable. The estimated
distance obtained from the geometric parameters was compared with the real distance of the object, and the
results showed a high degree of accuracy as seen in Figure 6. This suggests that the distance estimation scheme
derived from this simple algorithm is a valuable tool for robotic applications where distance measurement is
important, such as navigation and human-robot interaction.
Time [s]
−2 0 2 4 6 8 10 12
Figure 6. Direct distance from the robot to the person during a movement estimated from the tracking
strategy. The black curve represents the raw data obtained from each image analyzed, and the blue curve
corresponds to the data smoothed by means of a moving average of 20 points
The success of this method can be attributed to it is reliance on the fundamental principle of geometry,
which states that the larger the area of the triangle, the farther the distance between its vertices. By utilizing this
principle, our algorithm was able to provide robust and reliable estimates of the distance between the robot and
the object in its environment. These results open up new avenues for further research in the field of robotics,
and demonstrate the potential for using simple geometric principles to improve the performance of robotic
systems.
5. CONCLUSION
The people tracking strategy presented in this paper is a novel contribution to the field of small assistive
robots. The strategy’s unique combination of high performance and low computational and memory storage
cost makes it an attractive solution for the development of small assistive robots. The strategy mimics the
human brain’s behavior when processing visual information and utilizes a differential filter and a k-means
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6216-6227
10. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 6225
clustering algorithm to track objects in unstructured indoor environments. The results of the performance
evaluation showed that the strategy has a low percentage of false detections and accurately tracks the motion
parameters of the robot head. The strategy’s ability to store short-term memory of previous observations and to
construct a pattern image to detect movement is a testament to the robustness of the approach. The strategy’s
low computational cost makes it an attractive solution for small assistive robots, which are often limited by the
available processing power. Additionally, the strategy’s low memory storage cost makes it suitable for small
robots with limited memory capacity.
In future work, we plan to extend the scope of the strategy’s evaluation to include different scenar-
ios with different levels of complexity, such as tracking multiple objects simultaneously or tracking objects in
outdoor environments. We also plan to integrate the people tracking strategy into the control architecture of a
small assistive robot, to demonstrate its practical applicability. In conclusion, the people tracking strategy pre-
sented in this paper represents a significant step forward in the development of small assistive robots. The high
performance and low computational and memory storage cost make it a promising solution for the development
of future small assistive robots.
ACKNOWLEDGEMENT
This work was supported by the Universidad Distrital Francisco José de Caldas, in part through CIDC,
and partly by the Facultad Tecnológica. The views expressed in this paper are not necessarily endorsed by Uni-
versidad Distrital. The authors thank the research group ARMOS for the evaluation carried out on prototypes
of ideas and strategies.
REFERENCES
[1] O. A. Wudarczyk et al., “Robots facilitate human language production,” Scientific Reports, vol. 11, no. 1, Aug. 2021,
doi: 10.1038/s41598-021-95645-9.
[2] N. Fields, L. Xu, J. Greer, and E. Murphy, “Shall I compare thee... to a robot? An exploratory pilot study using
participatory arts and social robotics to improve psychological well-being in later life,” Aging and Mental Health,
vol. 25, no. 3, pp. 575–584, Mar. 2021, doi: 10.1080/13607863.2019.1699016.
[3] A. E. Eiben, E. Hart, J. Timmis, A. M. Tyrrell, and A. F. Winfield, “Towards autonomous robot evolution,” in Software
Engineering for Robotics, Cham: Springer International Publishing, 2021, pp. 29–51.
[4] D. Reverter Valeiras, X. Lagorce, X. Clady, C. Bartolozzi, S.-H. Ieng, and R. Benosman, “An asynchronous neu-
romorphic event-driven visual part-based shape tracking,” IEEE Transactions on Neural Networks and Learning
Systems, vol. 26, no. 12, pp. 3045–3059, Dec. 2015, doi: 10.1109/TNNLS.2015.2401834.
[5] W. Takano, “Annotation generation from IMU-based human whole-body motions in daily life behavior,” IEEE Trans-
actions on Human-Machine Systems, vol. 50, no. 1, pp. 13–21, Feb. 2020, doi: 10.1109/THMS.2019.2960630.
[6] D. Das, M. G. Rashed, Y. Kobayashi, and Y. Kuno, “Supporting human-robot interaction based on the level of
visual focus of attention,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 6, pp. 664–675, 2015,
doi: 10.1109/THMS.2015.2445856.
[7] N. F. Duarte, M. Rakovic, J. Tasevski, M. I. Coco, A. Billard, and J. Santos-Victor, “Action anticipation: reading the
intentions of humans and robots,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 4132–4139, Oct. 2018,
doi: 10.1109/LRA.2018.2861569.
[8] D. Fernandez-Chaves, J.-R. Ruiz-Sarmiento, A. Jaenal, N. Petkov, and J. Gonzalez-Jimenez, “Robot@virtualhome,
an ecosystem of virtual environments and tools for realistic indoor robotic simulation,” Expert Systems with Applica-
tions, vol. 208, Dec. 2022, doi: 10.1016/j.eswa.2022.117970.
[9] D. F. P. C, “Performance evaluation of ROS on the Raspberry Pi platform as OS for small robots,” Tekhnê, vol. 14,
no. 1, pp. 61–72, 2017.
[10] J. Castañeda and Y. Salguero, “Adjustment of visual identification algorithm for use in stand-alone robot navigation
applications,” Tekhnê, vol. 14, no. 1, pp. 73–86, 2017.
[11] P. Cigliano, V. Lippiello, F. Ruggiero, and B. Siciliano, “Robotic ball catching with an eye-in-hand single-
camera system,” IEEE Transactions on Control Systems Technology, vol. 23, no. 5, pp. 1657–1671, 2015,
doi: 10.1109/TCST.2014.2380175.
[12] K. Zhang, J. Chen, Y. Li, and Y. Gao, “Unified visual servoing tracking and regulation of wheeled mobile robots
with an uncalibrated camera,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 4, pp. 1728–1739, Aug. 2018,
doi: 10.1109/TMECH.2018.2836394.
[13] L. H. Juang and J. Sen Zhang, “Visual tracking control of humanoid robot,” IEEE Access, vol. 7, no. 1,
pp. 29213–29222, 2019, doi: 10.1109/ACCESS.2019.2901009.
A novel visual tracking scheme for unstructured indoor environments (Fredy Martı́nez)
11. 6226 ❒ ISSN: 2088-8708
[14] K.-H. Lee, J.-N. Hwang, G. Okopal, and J. Pitton, “Ground-moving-platform-based human tracking using visual
SLAM and constrained multiple kernels,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12,
pp. 3602–3612, Dec. 2016, doi: 10.1109/TITS.2016.2557763.
[15] E. Petrović, A. Leu, D. Ristić-Durrant, and V. Nikolić, “Stereo vision-based human tracking for robotic follower,”
International Journal of Advanced Robotic Systems, vol. 10, no. 5, May 2013, doi: 10.5772/56124.
[16] T. Sonoura, T. Yoshimi, M. Nishiyama, H. Nakamoto, S. Tokura, and N. Matsuhir, “Person following robot with
vision-based and sensor fusion tracking algorithm,” in Computer Vision, InTech, 2008.
[17] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, vol. 1, pp. 886–893,
doi: 10.1109/CVPR.2005.177.
[18] M. Gupta, S. Kumar, L. Behera, and V. K. Subramanian, “A novel vision-based tracking algorithm for a
human-following mobile robot,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 7,
pp. 1415–1427, Jul. 2017, doi: 10.1109/TSMC.2016.2616343.
[19] K. Wang, Y. Liu, and L. Li, “Vision-based tracking control of underactuated water surface robots without direct
position measurement,” IEEE Transactions on Control Systems Technology, vol. 23, no. 6, pp. 2391–2399, 2015,
doi: 10.1109/TCST.2015.2403471.
[20] J. P. C. de Souza, A. M. Amorim, L. F. Rocha, V. H. Pinto, and A. P. Moreira, “Industrial robot programming by
demonstration using stereoscopic vision and inertial sensing,” Industrial Robot, vol. 49, no. 1, pp. 96–107, 2022,
doi: 10.1108/IR-02-2021-0043.
[21] W. Ye, Z. Li, C. Yang, J. Sun, C. Y. Su, and R. Lu, “Vision-based human tracking control of a
wheeled inverted pendulum robot,” IEEE Transactions on Cybernetics, vol. 46, no. 10, pp. 2423–2434, 2015,
doi: 10.1109/TCYB.2015.2478154.
[22] F. Hernán, M. Sarmiento, J. Gómez, D. Alexander, and Z. Dı́az, “Concepto de robot humanoide antropométrico para
investigación en control,” Tecnura, vol. 19, no. SPE, pp. 55–65, 2015, doi: 10.14483/22487638.10372.
[23] E. D. Lasso, A. Patarroyo Sánchez, and F. H. Martinez, “Acoustic tracking system for autonomous robots based on
TDE and signal intensity,” Tecciencia, vol. 10, no. 19, pp. 43–48, 2015, doi: 10.18180/tecciencia.2015.19.7.
[24] M. Wang et al., “Accurate and real-time 3-D tracking for the following robots by fusing vision and ul-
trasonar information,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 3, pp. 997–1006, 2018,
doi: 10.1109/TMECH.2018.2820172.
[25] B. Li et al., “Vision-based mobile indoor assistive navigation aid for blind people,” IEEE Transactions on Mobile
Computing, vol. 18, no. 3, pp. 702–714, 2019, doi: 10.1109/TMC.2018.2842751.
[26] N. shi Yao et al., “Autonomous flying blimp interaction with human in an indoor space,” Frontiers of Information
Technology and Electronic Engineering, vol. 20, no. 1, pp. 45–59, 2019, doi: 10.1631/FITEE.1800587.
[27] S. Mehryar, P. Malekzadeh, S. Mazuelas, P. Spachos, K. N. Plataniotis, and A. Mohammadi, “Belief condensation
filtering for RSSI-based state estimation in indoor localization,” in IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), May 2019, pp. 8385–8389, doi: 10.1109/ICASSP.2019.8683560.
[28] D. Feng, C. Wang, C. He, Y. Zhuang, and X. G. Xia, “Kalman-filter-based integration of IMU and UWB for high-
accuracy indoor positioning and navigation,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 3133–3146, 2020,
doi: 10.1109/JIOT.2020.2965115.
[29] E. I. Al Khatib, M. A. K. Jaradat, and M. F. Abdel-Hafez, “Low-cost reduced navigation system for mobile
robot in indoor/outdoor environments,” IEEE Access, vol. 8, no. 1, pp. 25014–25026, 2020, doi: 10.1109/AC-
CESS.2020.2971169.
[30] Y. Liu and J. Miura, “KMOP-vSLAM: dynamic visual SLAM for RGB-D cameras using k-means and Open-
Pose,” in 2021 IEEE/SICE International Symposium on System Integration (SII), Jan. 2021, pp. 415–420,
doi: 10.1109/IEEECONF49454.2021.9382724.
[31] S. Liu, S. Wang, X. Liu, C.-T. Lin, and Z. Lv, “Fuzzy detection aided real-time and robust visual tracking
under complex environments,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 90–102, Jan. 2021,
doi: 10.1109/TFUZZ.2020.3006520.
[32] X. Xue, S. Huang, N. Li, and W. Zhong, “Resolvable cluster target tracking based on wavelet coefficients and
JPDA,” in 2021 International Symposium on Computer Technology and Information Science (ISCTIS), Jun. 2021,
pp. 330–336, doi: 10.1109/ISCTIS51085.2021.00074.
[33] J. Giordano, M. Lazzaretto, G. Michieletto, and A. Cenedese, “Visual sensor networks for indoor real-time surveil-
lance and tracking of multiple targets,” Sensors, vol. 22, no. 7, 2022, doi: 10.3390/s22072661.
[34] H. Shi, J. Yang, J. Shi, L. Zhu, and G. Wang, “Vision-sensor-assisted probabilistic localization method for indoor
environment,” Sensors, vol. 22, no. 19, 2022, doi: 10.3390/s22197114.
[35] A. Marie, H. G. A. Burton, and P. F. ois Loos, “Perturbation theory in the complex plane: Exceptional points and
where to find them,” Journal of Physics Condensed Matter, vol. 33, no. 28, 2021, doi: 10.1088/1361-648X/abe795.
[36] K. Kollom et al., “A four-country cross-case analysis of academic staff expectations about learning analytics in higher
education,” Internet and Higher Education, vol. 49, no. 2020, 2021, doi: 10.1016/j.iheduc.2020.100788.
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6216-6227
12. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 6227
[37] F. Mart´ınez, Colombia. Nao robot tracking people. (Apr. 20, 2019). Accessed: Apr. 10, 2023. [Online Video].
Available: https://youtu.be/Gnz0aRBoOsA.
BIOGRAPHIES OF AUTHORS
Fredy Martı́nez is a professor of control, intelligent systems, and robotics at the Universi-
dad Distrital Francisco José de Caldas (Colombia) and director of the ARMOS research group (Mod-
ern Architectures for Power Systems). His research interests are control schemes for autonomous
robots, mathematical modeling, electronic instrumentation, pattern recognition, and multi-agent sys-
tems. Martı́nez holds a Ph.D. in Computer and Systems Engineering from the Universidad Nacional
de Colombia. He can be contacted at email: fhmartinezs@udistrital.edu.co.
Holman Montiel is a professor of algorithms, embedded systems, instrumentation,
telecommunications, and computer security at the Universidad Distrital Francisco José de Caldas
(Colombia) and a researcher in the ARMOS research group (Modern Architectures for Power Sys-
tems). His research interests are encryption schemes, embedded systems, electronic instrumentation,
and telecommunications. Montiel holds a master’s degree in computer security. He can be contacted
at email: hmontiela@udistrital.edu.co.
Fernando Martı́nez is a doctoral researcher at the Universidad Distrital Francisco José
de Caldas focusing on the development of navigation strategies for autonomous vehicles using hi-
erarchical control schemes. In 2009 he completed his M.Sc. degree in Computer and Electronics
Engineering at Universidad de Los Andes, Colombia. He is a researcher of the ARMOS research
group (Modern Architectures for Power Systems) supporting the lines of electronic instrumentation,
control and robotics. He can be contacted at email: fmartinezs@udistrital.edu.co.
A novel visual tracking scheme for unstructured indoor environments (Fredy Martı́nez)