Automatic Motion Trajectory Analysis for Dual Human Interaction Using Video Sequences

Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

Abstract:

Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.). 

Keywords: Motion detection, motion tracking, trajectory analysis, video surveillance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107249

References:

[1] S. Saravanakumar, A. Vadivel, and C. G. Saneem Ahmed, “Multiple human object tracking using background subtraction and shadow removal techniques,” International Conference on Signal and Image Processing (ICSIP), pp. 79-84, 2010.
[2] P. P. Kuralkar, and V. T. Gaikwad, “Human object tracking using background subtraction and shadow removal techniques,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, no. 3, March 2012.
[3] F. Porikli, and O. Tuzel, “Human body tracking by adaptive background models and mean-shift analysis,” IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2003.
[4] P. Banerjee, and S. Sengupta, “Human motion detection and tracking for video surveillance,” Proceedings of the national Conference of Communications, IIT Bombay, Mumbai, pp. 88-92, 2008.
[5] R. S. Rakibe, and B. D. Patil, “Background subtraction algorithm based human motion detection,” International Journal of Scientific and Research Publications, vol. 3, no.5, 2013.
[6] W. Kim, and J. Lee, “Visual tracking using Snake based on target's contour information,” Industrial Electronics, Proceedings. ISIE 2001. IEEE International Symposium on IEEE, vol. 1, pp. 176-180, 2001.
[7] D. Comaniciu, V. Ramesh, and P. Meer, “Kernel based object tracking,” IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 25, no.5, pp. 564-557, 2003.
[8] F. Chang, L. Ma, and Y. Qiao, “Target tracking under occlusion by combining Integral-Intensity-Matching with multi-block-voting,” in Proc. of Lecture Notes, vol. 3644, pp. 77-86, 2005.
[9] P. F. Gabriel, et al., “The state of the art in multiple object tracking under occlusion in video sequences,” Advanced Concepts for Intelligent Vision Systems, pp. 166-73, 2003.
[10] Z. Li, A.K. Katsaggelos and B.Gandhi, “Fast video shot retrieval based on trace geometry matching,” IEE Proc.-Vis. Image Signal Process., vol. 152, no. 3, June 2005.
[11] H.-Y. M. Liao, D.-Y. Chen, C.-W. Su and H.-R. Tyan, “Real-time event detection and its application to surveillance systems,” Proceedings of IEEE International Symposium on Circuits and Systems, ISCAS, pp. 509 -512, 2006.
[12] D. H. Douglas and T. K. Peucker, “Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,” The Canadian Cartographer, vol. 10, no. 2, pp. 112-122, 1973.
[13] Y. Zhou and T. S. Huang, “’Bag of segments’ for motion trajectory analysis,” 15th IEEE International Conference on Image Processing, ICIP, pp. 757-760, 2008.
[14] H.-I. Suk, A.K. Jaim and S.-W. Lee, “A Network of Dynamic Probabilistic Models for Human Interaction Analysis,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 7, July 2011.
[15] H. Habe, K. Honda and M. Kidode, “Human Interaction Analysis Base on Walking Pattern Transitions,” Third ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC, pp. 1-8, 2009.
[16] N. Otsu, “A threshold selection method from gray-Level histogram,” IEEE Trans. Systems, Man and Cybernetics, vol. 9, no.1, pp. 62-66, 1979.
[17] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2rd Edition. New York: Prentice Hall, 2002.
[18] H. Freeman, Boundary Encoding and Processing, in Picture Processing and Psychopictorics, B S Lipkin and A Rosenfeld, Editors, Academic Press: New York. pp. 241-266, 1970.
[19] P. F. Felzensz and D. P. Huttenlocher, “Distance transforms of sampled functions,” Techniacl Report TR2004-1963, Computer Science Department, Cornell University, September 2004.
[20] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of basic Engineering, vol. 82, no. 1, pp. 35-45, 1960.