Computer Vision Technology and Expertise of Rhonda Software

1. Rhonda Software
Computer Vision (CV) Experience
Video analytics methods and algorithms
dedicated for continuous image processing
in fully-automated solutions

2. Computer Vision - Area of Expertise
Computer Vision is relatively new but most rapidly
growing domain of Rhonda's expertise. Since 2007
Rhonda has been doing research and development in
this area.
As the mainstream of CV R&D, Rhonda has released two
Audience Measurement products dedicated for accurate
people counting (myAudience-Count) and precise audience demographics
studying (myAudience-Measure).
Rhonda Software also offers CV custom-oriented solutions in many other
domains like:
• Object/pattern recognition
• Face detection
• Face landmarks automatic mark-up
• Human body detection & tracking
• Face recognition.
© 2013 Rhonda Software: www.rhondasoftware.com
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3. Beholder Computer Vision Framework
Platform for Developing Applications in CV Domain
• Cross-platform solution, active development under
Linux and Windows
• Continuous integration with overnight builds and
automatic testing
• Modular distributed architecture on top of ZeroMQ
messaging platform and Google protocol buffers data
serialization provides flexible allocation of system functions across system
processing nodes (e.g. offloading CPU-consuming key-point extraction
module from TI OMAP 3530 to Intel Atom-based notebook while keeping
other modules on the same nodes was a matter of couple hours including rebuilding and testing)
• Designed to work on low performance platform
• Unit test available for all algorithms
• Process real-time and prerecorded video
© 2013 Rhonda Software: www.rhondasoftware.com
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4. Beholder Computer Vision Framework
Analysis Stages & Algorithms
• Analysis stages can be transferred between different modules based on unique
solution requirements and hardware restraints.
Background
model
Object
tracking
Feature
Extraction
Face
detection
Face
Recognition
Statistics
and RDB
Media
presentation
• Combination of existing algorithms gives advantage to achieving result
Background
Object tracking
Face detection
Feature Extraction
Object recognition
Image Enhancement
Face
Recognition/classification
Media presentation
Gaussian
Color/Intensity
based histograms
Viola and Jones
Statistical models:
PCA/ICA
Pattern-based
recognition (Viola
and Jones)
Super resolution
Hidden Markov
Models
IP stream
Mean Shift
Mean shift/Cam
shift
Face tracker
GMM
Feature-based
recognition
(SURF/SIFT)
Deconvolution
Neural-networks
WEB
KDE
Key points/feature
based tracking
Morphological
analysis
Generalized Hough
Transform, Hough
Transform
Capture using PTZ
camera
“3D” models
Linux/QT
applications
Multiview tracking
(Homography and
cooperative
tracking)
TVL1
Contour-based
detection
ASM/AAM
Windows
applications
Pattern maps
Barcode detection
/recognition
AdaBoost/AdaSVM
Windows Media
Center
Optical flow
(Lucas–Kanade);
Motion detection
PTZ camera
Laplacianfaces
© 2013 Rhonda Software: www.rhondasoftware.com
Gabor wavelets
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5. Computer Vision Technologies
CV Methods in Our Area of Expertise
• Background subtraction – subtracting objects from complex/moving
background as part of object tracking/recognition
• Overlapped object tracking – object tracking of overlapping objects
• Multi-view tracking – tracking objects between different cameras
• Object recognition – recognition of objects on complex background
• Barcode detection and recognition – UPC-A Barcode recognition using
video stream from regular camera
© 2013 Rhonda Software: www.rhondasoftware.com
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6. Computer Vision Technologies
CV Methods in Our Area of Expertise (continue)
• Facial features extraction –
locating facial features from video
stream or still images
• Face detection, tracking and
recognition – including attention
recognition
• Demographic classification – age
and gender recognition from video
stream or still images
• People/object counting – using
top side view camera or horizontally
positioned camera
• Computer cluster for training algorithms – using dedicated cluster and MPI
based applications to train and test algorithms
© 2013 Rhonda Software: www.rhondasoftware.com
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7. Computer Vision Projects
Examples of Projects in CV Domain
• Semiconductor Insight – electronic microscope
image enhancement and de-blurring
• En-Vision America vision impaired helper,
based on Freescale I.MX31 ARM11
board:
• Barcode detection and barcode
recognition
• Bank note recognition
• Logo recognition
• Face detection
• Traffic monitoring system for major US photo enforcement company – traffic
lights phase detection under various illumination and weather conditions
• Rhonda audience measurement cross-platform solutions with support of
attention detection, gender / age recognition: myAudience-Measure and
myAudience-Count
© 2013 Rhonda Software: www.rhondasoftware.com
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8. Computer Vision Solutions
myAudience – Audience Measurement product details
myAudience-Measure is an automated
anonymous audience measurement product.
It can be used both with Digital Signage vehicles
and all kinds of static installations. This Computer
Vision solution gathers statistical information about
vehicle audience – i.e. it counts visitors in the
camera’s field of view, recognizes gender, age
group and attention of viewers.
myAudience-Count is a bi-directional people
traffic counter SW product that utilizes topmounted camera (USB and IP cams supported).
myAudience-Count shares operational
architecture, back-end server and software
updates infrastructure with myAudienceMeasure using same OS, data storage and
online Reports Portal.
© 2013 Rhonda Software: www.rhondasoftware.com
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9. Computer Vision Embedded
myAudience-Count ported to FPGA-based platform
One of the newest directions in research
and development activities conducted by
Rhonda Software concerns FPGA embedded
platform and its potential in acceleration of
computational intensive algorithms of
Computer Vision technology.
It was decided to start from porting the PC-based
myAudience-Count SW product on Lattice HDR-60
board powered by ECP3 FPGA chip.
100% compatible solution was redesigned to fit on a
single chip. Ported components include: Video capturing
HDR-enabled module, HW-accelerated Video analytics
pipeline, embedded Linux on new kernel, Web-server
optimized for AJAX-requests and Ethernet controller
implemented in programmable logic.
© 2013 Rhonda Software: www.rhondasoftware.com
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10. Computer Vision Embedded
DSP and GPGPU Development
Most of Computer Vision algorithms are
computationally intensive. Reduced CPU
performance on compact and embedded
systems brings developers to use DSP or GPU
if available.
DSP and GPU implementations may
have specific limitations but save a lot of CPU
power at the expense of additional hardware and
significant code redesign.
• Barcode Library on TI OMAP 3530 (Beagle Board), Angstrom Linux uses
on-board DSP via DSP Bridge driver to preprocess source barcode pictures
• Viola and Jones Face Detector from OpenCV library was ported to nVidia
CUDA GPGPU. Now it can run on GPU in parallel with other algorithms
running on CPU
• Gaussian Background Subtraction algorithm was also ported to CUDA
© 2013 Rhonda Software: www.rhondasoftware.com
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11. Effective Computer Vision
CV Algorithms Optimization
Rhonda engineers optimize computationally intensive algorithms for speed:
•
For the open source implementation of Viola and Jones Face Detector from
OpenCV it took 10 seconds to handle a 640x480 frame on I.MX31 ARM11. After the
optimization it takes 2 seconds for the same amount of input data, i.e. now it works 5
times faster
•
The same code on Intel P4 handled 2.72 frames per second before the optimization.
The optimized code makes 15 FPS, i.e. 5.5 times faster http://www.computer-visionsoftware.com/blog/2009/06/fastfurious-face-detection-with-opencv/
•
It took 4 days for Haar Training algorithm to create a cascade for the Viola and Jones
classifier on Intel Core 2 Duo. Parallel version of the algorithm developed in Rhonda
to work on a cluster of 11 computers do the same job 6.8 times faster (21 hour)
http://www.computer-vision-software.com/blog/2009/06/parallel-world-of-opencv/
•
For the open source implementation of SURF algorithm from OpenCV applied to bank
note recognition it took 4-12 seconds to handle a 640x480 frame on Intel P4. After the
optimization it takes 250-350 ms for the same amount of input data, i.e. now it works
16-34 times faster
© 2013 Rhonda Software: www.rhondasoftware.com
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12. Thank You!
Andrey Mischenko
CEO, Rhonda Software
Mobile: +7(423)257-1008
E-mail: ceo@rhondasoftware.com
Denis Pomogaev
VP of Technology Innovations
Mobile: +1(224)715-1154
E-mail: dp@rhondasoftware.com
© 2013 Rhonda Software: www.rhondasoftware.com
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