[1] [1] GORDON N J,SALMOND D J,SMITH A F M.Novel approach to nonlinear/non-gaussian Bayesian state estimation[C].Proc.of the Radar and Signal Processing,1993:107-113.

[2] [2] ISARD M,BLAKE A.Condensation-conditional density propagation for visual tracking[J].Int.J.Computer Vision,1998,29(1):5-28.

[5] [5] ISARD M,BLAKE A.Icondensation:unifying low-level and high-level tracking in a stochastic framework[C].Springer-Verlag Berlin Herdelberg 1998,Proc.of the Fifth European Conf.on Computer Vision,Berlin,1998:893-908.

[6] [6] PITT M K,SHEPHARD N.Filtering via simulation:auxiliary particle filter[J].J.of the American Statistical Associatin,1999,94(2):590-599.

[9] [9] BERZUINI C,BEST N.Dynamic conditional independence models and Markov chain Monte Carlo methods[J].J.of the American Statistical Association,1997,92(5):1403-1412.

[10] [10] BELVIKAN E,ACKLAM P J.Monte Carlo filters for non-linear state estimation[J].Automatica,2001,37(1):177-183.

[11] [11] COMANICIN D,RAMESH V,MEER P.Real-time tracking of non-rigid objects using Mean Shift[J].Computer Vision and Pattern Recognition,2002:142-149.

[12] [12] DORIN C,PETER M.Mean Shift:a robust approach toward feature space analysis[J].IEEE Transacions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.

[13] [13] ROBERT C T.Mean-shift blob tracking through scale space[C].Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Madison,Wisconsin,USA,2003,2:234-240.

[14] [14] NUMMIARO K,MEIER E K,VANGOOL L.A color -based Particle Filter[C].1st Int.Workshop on Generative-Model-Based Vision in Conjunction with ECCV'02,2002:53-60.

[15] [15] NUMMIARO K,MELER E K,VANGOOL L.Object tracking with an adaptive color-based particle filter[C].Symposium for Patter Recognition of the DAGM,2002:353-360.

[16] [16] AHERNE F,THACKER N,ROCKETT P.The Bhattaacharyya metric as an absolute similarity[J].Measure for Frequency Coded Data,Kybernetika,1997,32(4):1-7.