Abstract
This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author’s previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this paper a method of combining Bhattacharyya Coefficient (BC) and Kalman filter innovation term is proposed as the criteria for jointly detecting the occlusion occurrence. Fragmentation of target is introduced in order to closely monitor the occlusion development. In the course of occlusion, the Kalman predictor is applied to determine the location of the occluded target, and the criterion for checking the re-appearance of the occluded target is also presented. The proposed approach is put to test on a standard video sequence, suggesting the satisfactory performance in multi-target tracking.
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References
K. Chen, X. M. Zhao, G. Y. Jiang, and H. N. Joo. Video tracking of human-faces with occlusion using enhanced Camshift approach. IEEE Proceedings of the 2010 International Conference on Progress in Informatics and Computing (PIC 2010), Shanghai, China, 2010.
B. Hu, K. Chen, J. Y. Xu, and Y. Zhang. Video object tracking based on Mean-shift with self-adaptability search window and Kalman prediction. Joural of Optoelectronics & Laser, 20(2009)11, 1518–1522 (in Chinese). 胡波, 陈恳, 徐建瑜, 张云. 基于 Kalman 预测和 Mean-shift 算法的视频目标跟踪. 光电子·激光, 20(2009)11, 1518–1522.
K. Chen, B. Hu, C. G. Jhun, and R. E. Yang. A Bhattacharyya-factor based Camshift application for video fast mobile target tracking. IEEE Proceedings of the 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD2010), Yantai, China, 2010, Vol. 4. 1867–1871.
K. Chen, B Hu, C. G. Jhun, and Q. N. Huang. Enhanced mean-shift for fast state-varying video motion tracking using self-adaptive search window. IEEE Proceedings of 2010 3rd International Congress on Image and Signal Processing (CISP2010), Yantai, China, 2010, 312–316.
D. J. Jwo and T. S. Cho. A practical note on evaluation Kalman filter performance optimality and degradation. Applied Mathematics, and Computation, 193(2007), 483–505.
A. Bhattacharyya. On a measure of divergence between two statistical populations defined by probability distributions. Bulletin of the Calcutta Mathematical Society, 1943, 99–109.
Y. Wu, T. Yu, and H. Guang. Tracking appearances with occlusions. IEEE Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), Madison, WI, USA, 2003, 789–95.
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Supported by the Program for Technology Innovation Team of Ningbo Government (No. 2011B81002) and the Ningbo University Science Research Foundation (No. xkl11075).
Communication author: Chen Ken, born in 1962, male, Ph.D., Associate Professor.
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Chen, K., Jhun, C.G. Multi-target visual tracking and occlusion detection by combining Bhattacharyya Coefficient and Kalman filter innovation. J. Electron.(China) 30, 275–282 (2013). https://doi.org/10.1007/s11767-013-2152-0
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DOI: https://doi.org/10.1007/s11767-013-2152-0