Abstract：When tracking the moving targets in video image sequences, the existing particle filter is not satisfactory due to the particle degradation and particle diversity loss. An object tracking algorithm with particle filter is proposed. The multi-agent co-evolutionary mechanism is introduced into the particle resampling process and makes the particle become an agent with abilities of local perception, competitive selection and self-learning by redefining the particle agent and its local living environment. The resampling process is accomplished by the co-evolutionary behaviors among particles such as competition, crossover, mutation and self-learning. It ensures the particle validity and increases the particle diversity. Experimental results show that the proposed algorithm tracks the moving object accurately and robustly in complex video scenes.
李永平，王延江，齐玉娟. 基于多智能体协同进化的粒子滤波目标跟踪算法[J]. 模式识别与人工智能, 2011, 24(1): 57-63.
LI Yong-Ping, WANG Yan-Jiang, QI Yu-Juan. Object Tacking with Particle Filter Based on Multi-Agent Co-Evolution. , 2011, 24(1): 57-63.
[1] Comaniciu D, Ramesh V. Mean Shift and Optimal Prediction for Efficient Object Tracking // Proc of the International Conference on Information Processing. Vancouver, Canada, 2000: 70-73 [2] Comaniciu D, Ramesh V, Meer P. Real-Time Tracking of Non-Rigid Objects Using Mean Shift // Proc of the International Conference on Computer Vision and Pattern Recognition. Hilton Head, USA, 2000, Ⅱ: 142-149 [3] Weng S K, Kuo C M, Tu S K. Video Object Tracking Using Adaptive Kalman Filter. Journal of Visual Communication and Image Representation, 2006, 17(6): 1190-1208 [4] Jang D S, Jang S W, Choi H I. 2D Human Body Tracking with Structural Kalman Filter. Pattern Recognition, 2002, 35(10): 2041-2049 [5] Gustafsson F, Gunnarsson F, Bergman N. Particle Filters for Positioning, Navigation and Tracking. IEEE Trans on Signal Processing, 2002, 50(2): 425-437 [6] Chang C, Ansari R. Kernel Particle Filter for Visual Tracking. IEEE Signal Processing Letters, 2005, 12(3): 242-245 [7] Kang Jian, Si Xicai, Rui Guosheng. Particle Filtering Techniques Based on Bayesian Theorem. Modern Radar, 2006, 26(1): 34-36 (in Chinese) (康 健,司锡才,芮国胜.基于贝叶斯原理的粒子滤波技术概述.现代雷达, 2006, 26(1): 34-36) [8] Hu Shiqiang, Jing Zhongliang. Overview of Particle Filter Algorithm. Control and Decision, 2005, 20(4): 361-365 (in Chinese) (胡士强,敬忠良.粒子滤波算法综述.控制与决策, 2005, 20(4): 361-365) [9] Kwok N M, Fang Gu, Zhou Weizhen. Evolutionary Particle Filter: Re-Sampling from the Genetic Algorithm Perspective // Proc of the IEEE International Conference on Intelligent Robots and Systems. Singapore, Singapore, 2005: 2935-2940 [10] Zhang Yan, Shen Zhenkang, Qiao Shidong. An Improved Particle Filter. Signal Processing, 2008, 24(1): 256-259 (in Chinese) (张 焱,沈振康,乔士东.一种改进型的粒子滤波器.信号处理, 2008, 24(1): 256-259) [11] Jiao Licheng, Liu Jing, Zhong Weicai. Co-Evolution Computation and Multi-Agent System. Beijing, China: Science Press, 2006 (in Chinese) (焦李成,刘 静,钟伟才.协同进化计算与多智能体系统.北京:科学出版社, 2006) [12] Doucet A, Godsill S J, Andrieu C. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing, 2000, 10(3): 197-208 [13] Arulampalm M S, Maskell S, Gordon N. A Tutorial on Particle Filters for On-Line Non-Linear/Non-Gaussian Bayesian Tracking. IEEE Trans on Signal Processing, 2002, 50(2): 174-188 [14] Comaniciu D, Ramesh V, Meer P. Kernel-Based Object Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577 [15] Pitt M K, Shephard N. Filtering via Simulation: Auxiliary Particle Filters. Journal of American Statistical Association. 1999, 94(446): 590-591
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