itz a Low cost monitoring self adaptive method using background subraction. Our project is based on security that is used to monitor the moving objects and store the images … notify the owner about the slight changes by sending a message to the owner on his/her mobile phone … For this we are making use of BACKGROUND SUBTRACTION METHOD

1. By
Nazneen begum
Ateeqa jabeen
Shabana begum

2. 
INTRODUCTION

GOAL

EXISTING SYSTEM

PROPOSED SYSTEM

DESCRIPTION

TECHICAL SPECIFICATIONS

WHY WE NEED THIS ?

CONCLUSION

REFERENCES

3. 




Security is a major aspect in todays life…
Every where in every field we need to be secure or provide
security so as to avoid any
major losses…
Our project is based on security that is
used to monitor the moving objects and
store the images …
notify the owner about the slight changes by sending a
message to the owner on his/her mobile phone …
For this we are making use of BACKGROUND
SUBTRACTION METHOD

4. 
background subtraction is the process of separating out
foreground objects from the background in a sequence of
image frames.

Background subtraction is a widely used approach for
detecting moving objects from static cameras.

5. 
Fundamental logic for detecting moving
objects from the difference between the current
frame and a reference frame, called
“background image” and this method is known
as FRAME DIFFERENCE METHOD

6. challenges are associated with background
modeling.
 Dynamic backgrounds
 Gradual illumination changes
 Sudden illumination changes
 Shadows
 Another challenge is that many moving
foregrounds can appear simultaneously with
the above non-static problems.

7. Name
Background subtraction algorithm
CB
codebook-based technique in the
paper
MOG
mixture of Gaussians by Stauffer &
Grimson (1999)
KER and KER.RGB*
non-parametric method using
Kernels by Elgammal et al. (2000).
UNI
unimodal background modeling by
Horprasert et al.(1999).

9. 

CCTV cameras are used.
There is a need for human to interact for
knowing about the changes in the current
surveillance systems.

10. 

It is not a fast secured monitored due to the
time delay taken for human interaction.
Due to time delay there is a problem in
updating of information.

11. The various disadvantages of Existing System are listed
below :

Highly hardware cost so cost effective and Less secure.

Needs human interaction for monitoring.

Lacks computation capability while
monitoring

12. 

The system provides a low-cost intelligent mobile
phone-based video surveillance solution using moving
object recognition technology.
A self-adaptive background model that can
update automatically and timely to adapt to the slow
and slight changes of natural environment is detailed.

13. 

the mobile phone will automatically notify the
central control unit or the user through SMS or
other means
Here svm and canny edge detection combined

14. 



Low maintenance cost
The key of this method lies in the initialization and
update of the background image/video.
Effective method to initialize the background, and
update the background in real time.
This system usage for capture accurate image/video.

15. 

Background modeling and subtraction is a natural
technique for object detection .
We propose a pixel wise background modeling and
subtraction technique using multiple features, where
generative and discriminative techniques are combined
for classification.

16. •A pixel wise generative background model is obtained
for each feature efficiently and effectively by Kernel
Density Approximation (KDA).
•Background subtraction is performed in a
discriminative manner using a Support Vector Machine
(SVM) over background likelihood vectors for a set of
features.
The proposed algorithm is robust to
shadow, illumination changes, spatial variations of
background.

20. Web camera
Frame Separation
Image Sequence
The current frame
image
Background
Frame image
Background
Subtraction
Moving Object
Reprocessing
Shape Analysis
Send SMS
Background
Update

21. Software requirement
Operating System
Technology
: Windows XP
: Java(swing), JMF

22. Hardware Requirement:




Processor
: > 2 GHz
Ram
: 1 GB
Hard Disk
: 80 GB
GSM Modem , Web Camera.

23. 



video surveillance.
traffic monitoring.
Human detection.
video editing.

24. Conclusion :
•Low cost adaptive method
•No need for monitoring
•Both software and hardware are used
Future Work:
•Velocity calculation of moving object
•View the images on mobile phone.

25. 



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C. Stauffer and W.E.L. Grimson, “Learning Patterns of Activity Using RealTime Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 22, no. 8, pp. 747-757, Aug. 2000.
B. Han, D. Comaniciu, and L. Davis, “Sequential Kernel Density
Approximation through Mode Propagation: Applications to Background
Modeling,” Proc. Asian Conf. Computer Vision, 2004.
D.S. Lee, “Effective Gaussian Mixture Learning for Video Background
Subtraction,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27,
no. 5, pp. 827-832, May 2005.
Z. Zivkovic and F. van der Heijden, “Efficient Adaptive Density Estimation
Per Image Pixel for Task of Background Subtraction,” Pattern Recognition
Letters, vol. 27, no. 7, pp. 773-780, 2006.
P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of
Simple Features,” Proc. IEEE Conf. Computer Vision and Pattern Recognition,
pp. 511-518, 2001.