The week's pick
Random Articles
Reseach Article
Video Surveillance System for Security Applications
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 1 |
| Year of Publication: 2013 |
| Authors: Vidya A. S, V. K. Govindan |
10.5120/12849-9294
|
Vidya A. S, V. K. Govindan . Video Surveillance System for Security Applications. International Journal of Computer Applications. 74, 1 ( July 2013), 17-24. DOI=10.5120/12849-9294
Abstract
Computer Vision (CV) deals with replacement of human interpretation with computer based interpretation. It automatically analyses, reconstruct, and recognise objects in a scene from one or more images. Video surveillance is a topic in CV dealing with the monitoring of humans and their behaviours to analyze the habitual and unauthorized activities. An efficient video surveillance system detects moving foreground objects with lowest False Alarm Rates (FAR). This paper makes two proposals: one to detect the foreground in the video and the other to detect humans for surveillance applications. The proposed approach of foreground detection employs computations in YCbCr colour space for detecting moving objects in CCTVs. This system can handle slight camera movement and illumination changes. After foreground detection, the silhouette obtained is analysed and classified to determine whether it is humans or non-humans. In computer vision, usually human detection is based on human face, the head, and the entire body including legs as well as the human skin. In this work, the detection of humans is done based on the ratio of upper body and total height of silhouette. The precision and recall performance measures of the approach are computed and found to be superior to the existing Mixture of Gaussian approach in the literature.
References
- Rosin, P and Ellis, T, "Detecting and classifying intruders in image sequences", in British Machine Vision Conference, 1991
- Sugandi, B. and Kim, H. and Tan, J. K. and Ishikawa, S. ,"Tracking of moving objects by using a low resolution image", Innovative Computing, Information and Control, 2007. ICICIC'07. Second International Conference pp. 408—408, 2007
- Srinivasan,K. and Porkumaran, K. and Sainarayanan,G. , "Improved background subtraction techniques for security in video applications" Anti-counterfeiting, Security, and Identification in Communication, 2009. ASID 2009. 3rd International Conference on pp. 114--117, 2009
- Stauffer, C. ; Grimson, W. E. L. , "Adaptive background mixture models for real-time tracking," Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on , vol. 2, no. , pp. 2 vol. (xxiii+637+663), 1999
- Wren, C. and Azarbayejani, A. and Darrell, T. and Pentland, A. ,"Pfinder: real-time tracking of the human body" Automatic Face and Gesture Recognition, 1996. , Proceedings of the Second International Conference pp. 51—56,1996
- Stauffer, C. and Grimson, W. E. L. ,"Learning patterns of activity using real-time tracking",Pattern Analysis and Machine Intelligence, IEEE Transactions volume- 22,number- 8 pp. 747—757,2000
- Haritaoglu, I. and Harwood, D. and Davis, L. S, "W4: Real-time surveillance of people and their activities", Pattern Analysis and Machine Intelligence, IEEE Transactions volume 22, number=8, pp. 809—830, 2000
- K H Sage, K M Wickham, "Estimating performance limits for an intelligent scene monitoring system (ISM) as a perimeter intrusion detection system (PIDS)", in International Carnahan Conference on Security Technology, 1994
- Sebastian, Patrick, Vooi Voon Yap, and Richard Comley. "Colour Space Effect on Tracking in Video Surveillance. " International Journal on Electrical Engineering and Informatics 2. 4 (2010): 306-320.
- Zhujie; Yu, Y. L. ; , "Face recognition with eigenfaces," Industrial Technology, 1994. , Proceedings of the IEEE International Conference on , vol. , no. , pp. 434-438, 5-9 Dec 1994 doi: 10. 1109/ICIT. 1994. 467155
- Ali, Syed Sohaib, M. F. Zafar, and Moeen Tayyab. "Detection and Recognition of Human in Videos Using Adaptive Method and Neural Net. " In Soft Computing and Pattern Recognition, 2009. SOCPAR'09. International Conference of, pp. 604-609. IEEE, 2009.
- Osuna, Edgar, Robert Freund, and Federico Girosit. "Training support vector machines: an application to face detection. " In Computer Vision and Pattern Recognition, 1997. Proceedings. , 1997 IEEE Computer Society Conference on, pp. 130-136. IEEE, 1997.
- Jones, Michael J. , and James M. Rehg. "Statistical color models with application to skin detection. " Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on. . Vol. 1. IEEE, 1999.
- Chai, D. ; Bouzerdoum, A. ; , "A Bayesian approach to skin color classification in YCbCr color space," TENCON 2000. Proceedings , vol. 2, no. , pp. 421-424 vol. 2, 2000
- Jiang, Z. and Huynh, DQ and Moran, W. and Challa, S,"Tracking pedestrians using smoothed colour histograms in an interacting multiple model framework" Image Processing (ICIP), 2011 18th IEEE International Conference pp. 2313—2316, 2011
- N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893, June 2005.
- Z. Jiang, D. Q. Huynh,W. Moran, S. Challa, and N. Spadaccini, "Multiple pedestrian tracking using colour and motion models," Digital Image Computing: Techniques and Applications, pp. 328–334, Dec. 2010.
- Ellis, T. J, P. Golton,, "Model based vision for automatic alarm interpretation", in International Carnahan Conference on Security Technology 1990 pp. 62-67
- Viola, Paul, and Michael J. Jones. "Robust real-time face detection. " International journal of computer vision 57. 2 (2004): 137-154.
Index Terms
Keywords