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http://dx.doi.org/10.5391/JKIIS.2012.22.6.741

Multiple Moving Objects Detection and Tracking Algorithm for Intelligent Surveillance System  

Shi, Lan Yan (Department of Control and Robotics Engineering, Kunsan National University)
Joo, Young Hoon (Department of Control and Robotics Engineering, Kunsan National University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.22, no.6, 2012 , pp. 741-747 More about this Journal
Abstract
In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.
Keywords
Intelligent surveillance System; Multiple targets; Kalman filter; Histogram; Color-based tracking algorithm;
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1 D. Zhou and H. Zhang, "Modified GMM background modeling and optical flow for detection of moving objects," IEEE International Conference, pp. 2224-2229, 2005.
2 X. Weihua, X. Lei, L. Junfeng, and Z. Xinlong, "Moving object detection algorithm based on background subtraction and frame differencing," Control Conference (CCC), pp. 3273-3276, 2011.
3 X. M. Dong and K.. Yuan, "A robust Cam Shift tracking algorithm based on multi-cues fusion," ICACC 2010, pp. 521-524, 2010.
4 J. S. Kim, D. H. Yeom, and Y. H. Joo, "Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems," IEEE Transactions Consumer Electronics, vol. 57, no. 3, pp. 1165-1170, 2011, 08.   DOI
5 A. Shimoide, I. Yoon, M. Fuse, H. C. Beale, and R. Singh, "Automated behavioral phenotype detection and analysis using color-based motion tracking," Computer and Robot Vision. The 2nd Canadian Conference, pp. 370-377, 2005.
6 C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, "Fall Detection from Human Shape and Motion History using Video Surveillance," 21st International Conference on Advanced Information Networking and Applications, pp. 875-880, 2007.
7 J. Zhao, W. Qiao, G. Z. Men, "An approach based on mean shift and KALMAN filter for target tracking under occlusion," Machine Learning and Cybernetics, 2009 International Conference, pp. 2058-2062, 2009.
8 Y. Shiu, N. G. Cho, P. C. Chang, and C. C. Kuo, "Robust on-line beat tracking with kalman filtering and probabilistic data association(KF-PDA)," IEEE Trans. on, vol. 54, issue. 3, pp. 1369-1377, 2008.
9 H. P. Zhu, Z. Q. Wang, C. Z. Wu, C. T. Wang, and Y. F. Fan, "Target tracking using Kalman Filter Embedded Trust Region," International Conference on Test and Measurement, ICTM '09, vol. 1, pp. 119-122, 2009.
10 J. U. Cho, S. H. Jin, X. D. Pham, D. K. Kim, and J. W. Jeon, "A real-time color feature tracking system using color histograms," Control, Automation and Systems, 2007. ICCAS'07, pp. 1163-1167, 2007.