detection of skeptical activities in ptas using real time surveillance system
M. Saravana Kumar,G. Suresh Kumar,K. Pavithradevi
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:2 Issue:3 Year: 08 April,2014 Pages:130-143
Abstract
Detection of skeptical activities in public transport areas using real time video surveillance system has attracted an increasing level of care. A framework that contains video data receives from a fixed color camera installed at a particular location. The noise from video frames is removed by using Gaussian filtering. The foreground blob is extracted from video frames using background subtraction method. The framework obtains 3-D object level information by detecting and tracking persons and luggage in the scene. The actions of public are identified and clustered in a crowd scene by using unsupervised learning k-means clustering and force field model. The features are extracted from the frames using Gabor algorithm, histogram of gradient and SIFT. The different variants of behavior that is relevant to security in public areas such as abandoned luggage, fighting, fainting, and loitering. The experimental results are to demonstrate the outstanding performance, fast object tracking and low computational complexity
Kewords
crowd behavior abnormal events staged matching k-means clustering force field model objects tracking occlusion
Reference
[1] M. Elhamod, Member, IEEE, and M. D. Levine, Life Fellow, IEEE “Automated Real-Time Detection of Potentially Suspicious Behavior in Public Transport Areas”, IEEE Transaction on Intelligent Transport systems, vol. 14, no. 2, June 2013. [2] H. Weiming, T. Tieniu, W. Liang and S. Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors”, IEEE Transaction System, Man, Cybern. C, Appl. Rev., Vol. 34, no. 3, pp. 334-352, Aug. 2004. [3] G. L. Foresti, C. Micheloni, L. Snidaro, P. Remagnino and T. Ellis, “Active video- based Surveillance System: The low-level image and video processing techniques needed for implementation”, IEEE Signal Process. Mag., Vol. 22, no. 2, pp. 25-37, Mar. 2005. [4] N. T. Siebel and S. J. Maybank, “The ADVISOR visual surveillance system”, in Proc. ECCV Workshop ACV, 2004, pp. 103-111. [5] A. Singh, S. Sawan, M. Hanmandlu, V. K. Madasu and B. C. Lovell,”An abandoned object detection system based on dual background segmentation”, in Proc. 6th IEEE Int. Conf. AVSS, 2009, pp. 352-357. [6] P. Guler, “Automated Crowd Behavior Analysis for Video Surveillance Application”, Informatics system, Thesis, Middle East University, Sep. 2012. [7] D. Weinland, R. Ronfard, and E. Boyer, A survey of vision-based methods for action representation, segmentation and recognition, comput. Vis. Image Underst., vol. 115, no. 2, pp. 224-241, Feb. 2011.
