International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 3 | Issue 4 | Year 2012 | Article Id. IJCTT-V3I4P102 | DOI : https://doi.org/10.14445/22312803/IJCTT-V3I4P102

Remote Based Intelligent Video Surveillance System

Sahithi, Venkat Mutyalu

Citation :

Sahithi, Venkat Mutyalu, "Remote Based Intelligent Video Surveillance System," International Journal of Computer Trends and Technology (IJCTT), vol. 3, no. 4, pp. 437-439, 2012. Crossref, https://doi.org/10.14445/22312803/IJCTT-V3I4P102

Abstract

The surveillance detection related events from video input is a sophisticated technology in real time security related applications. Many current and Existing solutions to this problem are simply slight variations on frame differencing concept. This proves to be difficult to configure and operate effectively. This Proposed sytem presents a new approach to this problem based on extracting and classifying the background contents of each video frame using a CAM equipped with a standard framegrabber. The action of objects classified as people is further categorized into a series of events such as person leaving ,entering ,deposits object, and so forth.A background model is used to obtain candidate surveillance objects from input video.

Keywords

Eigen faces Eigen Vector, Eigen Value, Neural Network, Back Propagation, Facial Expression Recognition System, FERS.

References

[1] Friedman, N. and Russell, S. 1997. Image segmentation in video sequences: a probabilistic approach. In Proc. 13th Conf. Uncertainty in Artificial Intelligence, (Brown University, Providence, Rhode Island, USA, August 1-3, 1997). Morgan Kaufmann, San Francisco, CA, 175 - 181.
[2] Bodor, R., Bennett, J. and Papanikolopoulos, N. 2006. Vision-Based Human Tracking and Activity Recognition. Retrieved April 4, 2006, from Artifical Intelligence, Robotics and Vision Laboratory, Department of Computer Science and Engineering, University of Minnesota: http://mha.cs.umn.edu/Papers/Vision_Tracking_Recognition. pdf
[3] Wei, G. and Sethi, I. K. 2000. Omni-face detection for video/image content description. In Proceedings of the 2000 ACM Multimedia Workshops, (Los Angeles, California, United States, 2000). ACM Press, New York, NY, USA, 185-189.
[4] Stauffer, C. and Grimson, W. 2000. Adaptive background mixture models for real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, (2000). IEEE Computer Society, Los Alamitos, CA, 747- 757.
[5] Hu, W., Tan, T., Wang, L. and Maybank, S. 2004. A survey on visual surveillance of object motion and behaviours. IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 34, no. 3, (August 2004). IEEE Systems, Man, and Cybernetics Society, 334-352.
[6] Sidenbladh, H. 2004. Detecting Human Motion with Support Vector Machines. In 17th International Conference on Pattern Recognition (ICPR'04), 2, (2004). IEEE Computer Society, Los Alamitos, CA, 188-191.
[7] Lipton, A. J., Fujiyoshi, H. and Patil, R. S. 1998. Moving target classification and tracking from real-time video. In Proc. IEEE Workshop on Applications of Computer Vision, (Princeton, NJ, USA, October 1998). IEEE Computer Society, Los Alamitos, CA, 8–14.
[8] Cucchiara, R., Grana, C., and Tardini, G. 2004. Trackbased and object-based occlusion for people tracking refinement in indoor surveillance. In Proceedings of the ACM 2nd international Workshop on Video Surveillance and Sensor Networks (New York, NY, USA, October 15, 2004). VSSN '04. ACM Press, New York, NY, 81-87.  
[9] Anomaly Detection and Prediction of Human Actions in a Video Surveillance Environment, thesis   Nemanja Spasic December 2007. 
[10] Video Content Analysis for Surveillance Applications, D.M. Lyons,T. Brodsky, A. Elgammal.