[1] [1] TIAN J, QIAN J SH, LI SH Y, et al.. Visual tracking with adaptive multi-cue fusion particle filter［J］. Opt. Precision Eng., 2010,18(10): 2254-2261. (in Chinese)

[2] [2] ZHOU B, WANG J ZH, SHEN W. Fast object tracking with multi-bandwidth Mean Shift［J］. Opt. Precision Eng., 2010,18(10): 2297-2305. (in Chinese)

[3] [3] LI F, SHI G M, ZHANG L, et al.. Variable bandwidth kernel particle filtering used for optoelectronic target tracking［J］. Opt. Precision Eng., 2010,18(3): 716-722. (in Chinese)

[4] [4] WANG G L, LIU J G. Moving object tracking with multi-degree-of-freedom based on particle filters［J］. Opt. Precision Eng., 2011,19(4): 864-869.(in Chinese)

[5] [5] GORDON N, SALMOND D, SMITH A. Novel approach to nonlinear/non-gaussian bayesian state estimation［J］. IEE Proceedings on Radar and Signal Processing, 1993,140(2): 107-113.

[6] [6] TING Y, YING W. Collaborative tracking of multiple targets ［C］. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Press,1995: 1-8.

[7] [7] CHANG C, ANSARA R. Kernel particle filter for visual tracking ［J］. IEEE Signal Processing Letters,2005,12(3): 242-245.

[8] [8] SHAN C F, TAN T N, WEI Y CH. Real-time hand tracking using a mean-shift embedded particle filter ［J］. Pattern Recognition,2007,40: 1958-1962.

[9] [9] PATRICK P, VERMAAK J. Data fusion for visual tracking with particle ［J］. IEEE, 2004,92(3): 495-513.

[10] [10] JIANG Y X, JIN Y X, WANG X T, et al.. Moving object detection based on Markov Random Field model ［J］. Computer Engineering and Applications, 2010,46(15): 182-184. (in Chinese)

[11] [11] PARAGIOS N, RSMESH V. A MRF-based approach for real-time subway monitoring ［C］. Processing of Computer Vision and Pattern Recognition, 2001: 295-313.

[12] [12] XUE CH, ZHU M, CHEN A H. Robust object tracking based on improved Mean-shift algorithm ［J］. Opt. Precision Eng., 2010,18(1): 234-239. (in Chinese)