Detection of Objects in Motion—A Survey of Video Surveillance

DOI: 10.4236/ait.2013.34010   PDF   HTML     14,490 Downloads   23,386 Views   Citations


Video surveillance system is the most important issue in homeland security field. It is used as a security system because of its ability to track and to detect a particular person. To overcome the lack of the conventional video surveillance system that is based on human perception, we introduce a novel cognitive video surveillance system (CVS) that is based on mobile agents. CVS offers important attributes such as suspect objects detection and smart camera cooperation for people tracking. According to many studies, an agent-based approach is appropriate for distributed systems, since mobile agents can transfer copies of themselves to other servers in the system.

Share and Cite:

J. Raiyn, "Detection of Objects in Motion—A Survey of Video Surveillance," Advances in Internet of Things, Vol. 3 No. 4, 2013, pp. 73-78. doi: 10.4236/ait.2013.34010.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. T. Collins, A. J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto, O. Hasegawa, P. Burt and L. Wixson, “A System for Video Surveillance and Monitoring,” Robotics Institute, Carnegie Mellon University, Pittsburgh, 2000.
[2] I. Haritaoglu, D. Harwood and L. S. Davis, “W4: Real- Time Surveillance of People and Their Activities,”IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, 2000, pp. 809-830.
[3] S. Kwak and H. Byun, “Detection of Deominant Flow and Abnormal Events in Surveillance Video,” Optical Engineering, Vol. 50, No. 2, 2011. pp. 1-8.
[4] Z. Xu and H. R. Wu, “Smart Video Surveillance System,” Proceedings of the IEEE International Conference on Industrial Technology, 14-17 March, pp. 285-290.
[5] S. Aramvith, et al., “Video Processing and Analysis for Surveillance Applications,” International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2009), 7-9 January 2009, Kanazawa, pp. 607-610.
[6] P. Bottoni, “A Dynamic Environment for Surveillance,” Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction, Uppsala, 24-28 August, 2009, pp. 892-895.
[7] N. M. Oliver, B. Rosario and A. P. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, 2000, pp. 831-843.
[8] D. Weinland, R. Ronfard and E. Boyer, “A Survey of Vision-Based Methods for Action Representation, Segmentation and Recognition,” Computer Vision and Image Understanding, Vol. 115, No. 2, 2011. pp. 224-241.
[9] I. Karaulova, P. Hall and A. Marshall, “A Hierarchical Model of Dynamics for Tracking People with a Single Video Camera,” Proceedings of the British Machine Vision Conference, 2000, pp. 262-352.
[10] Y. Ren, et al., “Detection and Tracking of Multiple Target Based on Video Processing,” 2009 Second International Conference on Intelligent Computation Technology and Automation, Changsha, 10-11 October 2009, pp. 586- 589.
[11] M. B. Augustin, S. Juliet and S. Palanikumar, “Motion and Feature Based Person Tracking in Surveillance Videos,” Proceedings of ICETECT 2011, Tamil Nadu, 23-24 March 2011, pp. 605-609.
[12] T. J. Broida and R. Chellappa, “Estimation of Object Motion Parameters from Noisy Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 1, 1986, pp. 90-99.
[13] Y. Su, et al., “Surveillance Video Sequence Segmentation Based on Moving Object Detection,” 2009 Second International Workshop on Computer Science and Engineering, Qingdao, 28-30 October 2009, pp. 534-537.
[14] C. Wren, A. Azarbayejani, T. Darrell and A. Pentland, “Pfinder: Real-Time Tracking of the Human Body,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 780-785.
[15] M. Ahmad and S.-W. Lee, “HMM-Based Human Action Recognition Using Multi View Image Sequences,” International Conference on Pattern Recognition, Vol. 1, 2006, pp. 263-266.
[16] Y. Kuno, T. Watanabe, Y. Shimosakoda and S. Nakagawa, “Automated Detection of Human for Visual Surveillance System,” Proceedings of the 13th International Conference on Pattern Recognition, Vienna, 25-29 August 1996, pp. 865-869.
[17] H. Gou, et al., “Implementation and Analysis of Moving Objects Detection in Video Surveillance,” Proceedings of the 2010 IEEE International Conference on Information and Automation, Harbin, 20-23 June 2010, pp. 154-158.
[18] S. Wang et al., “A Mobile Agent Based Multi-Node Wireless Video Collaborative Monitoring System,” The 3rd International Conference on Advanced Computer Theory and Engineering, Chengdu, 20-22 August 2010, pp. 35-39.
[19] H. Kakiuch, et al., “Detection Methods Improving Reliability of Automatic Human Tracking System,” 2010 4th International Conference on Emerging Security Information, Systems and Technologies, Washington DC, 2010, pp. 240-246.
[20] W. Y. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Surveys, Vol. 35, No. 4, 2003, pp. 399-458.
[21] T. S. Ling, L. K. Meng, L. M. Kuan, Z. Kadim and A. A. Baha Al-Deen, “Colour Based Object Tracking in Surveillance Application,” Proceedings of the International Multi-Conference of Engineers and Computer Scientists, Hong Kong, 18-20 March 2009, pp. 459-464.
[22] B. Schiele, “Model-Free Tracking of Cars and People Based on Color Regions,” Image and Vision Computing, Vol. 24, No. 11, 2006, pp. 1172-1178.
[23] Z. Zhang, “Head Detection for Video Surveillance Based on Categorical Hair and Skin Colour Models,” The 16th IEEE International Conference on Image Processing, Cairo, 7-10 November 2009, pp.1137-1140.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.