Mean Shift及相关算法在视频跟踪中的研究
|
摘要
在视觉跟踪领域,Mean Shift算法是一个非常优秀的算法,是国外最近几年才发展起来的。国外研究者比较多,国内的很少,去年才见有几篇文章发表。初入跟踪领域,走过了很多弯路后,后来在Mean Shift算法上找到了突破口,并在该算法的研究中投入大量的时间,是以论文的题目以Mean Shift命名。Mean Shift在跟踪领域有很多很好的性质,比如实时性好、对遮挡、目标变形鲁棒性好等,但它也有一些缺点。作者针对性地对这些缺陷做了较好的改进。论文虽以Mean Shift命名,但涉及内容已超出Mean Shift的范畴。
第三章针对Mean Shift算法不能跟踪快速目标的特点,提出Mean Shift和卡尔曼滤波器相结合的算法,卡尔曼滤波器预测目标在本帧的可能位置,Mean Shift算法在该位置邻域内搜索,算法对快速运动的目标的跟踪效果良好,而且对遮挡问题也有很好的处理。
第四章提出Mean Shift的模板更新算法,Mean Shift没有模板更新的能力。模板更新对目标的跟踪至关重要,但没有通用的模板更新算法,一般都是针对某种跟踪算法如何解决模板更新的问题。本文提出一种基于卡尔曼滤波器组的Mean Shift模板更新算法。模板的元素取自目标特征值的概率,通过48个卡尔曼滤波器可以跟踪所有特征值的概率变化。算法构造巧妙,由于使用了较少的卡尔曼滤波器,算法实时性好,鲁棒性更好。
第五章提出核直方图的粒子滤波器目标跟踪算法。算法的系统动态模型具有对速度的学习能力,这样可以减少粒子的维数和所需要的粒子数。观测模型以Mean Shift算法对目标特征值的描述为基础,本文设计了一种新的模板更新算法,更新算法充分利用了粒子滤波器计算的中间值,因而没有增加算法复杂性。模板更新能够使得观测值的获得更可靠,因而提高了算法的鲁棒性。
第六章提出基于Mean Shift粒子滤波器的算法。粒子滤波器算法的主要缺点是需要大量的粒子来近似描述目标的状态,使得算法非常费时。新算法利用Mean Shift算法在重采样之前将粒子收敛到集合靠近目标真实状态的区域内,由于每
Mean Shift is a very good algorithm in visual target tracking area. Many scholars in foreign countries has developed this algorithm in recent years, however, few scholars study it in our country. A few articles can be found last year ever.I experienced many difficulties, when I entered the realm of target tracking. Finally I found the Mean Shift algorithm. I have spent many times in this algorithm, so my dissertation is named Mean Shift. The algorithm has many advantages, for example: very good real-time, robust for occlusion and target distortion, however, it has a few defects. Many improvements are done for these defects. My dissertation is named Mean Shift although, but the content is out the scope of Mean Shift.In third chapter, an algorithm combining kalman filter and Mean Shift is bring forward, arm at the Mean Shift couldn't tracking the fast moving target. Kalman filter is used to forecast possible position of target, then Mean Shift search the real position near the possible position. The algorithm has good effect to fast moving target, and can deal well to occlution.In fourth chapter, a template update algorithm of Mean Shift is put forward. Template update is very important to target tracking. There isn't general algorithm for it however, many template update is arm to given tracking algorithm. In this chapter, an algorithm of template update of Mean Shift based a group kalman filter is proposed. The element of template is probability of eigenvalue of target. These probability are acquire by a kalman filter group which had 48 kalman filters. The whole algorithm conformation is very skilled. Because the number of kalman filter is lesser, algorithm is real-time, and robust.In fifth chapter, an algorithm based on kernel histogram particle filter proposed. System dynamic model of algorithm can learn the velocity of target, so the dimension of particle is reduced and the required particle is very few. Observation model of algorithm is based on Mean Shift describing the eigenvalue. In this chapter, a new template update algorithm is devised. New algorithm makes the best of middle value of particle filter, so that the
complexity of algorithm is not added. Template update enable to get more credible observation, so improve the robustness of algorithm.In sixth chapter, an algorithm based on Mean Shift particle filter is proposed. The main disadvantage of particle filter is it require so many particles to approximately describing state of target, that the real-time of algorithm is not ensured. New algorithm use Mean Shift algorithm to muster the particle to area of real state after re-sample .Because the particle describe the state of more rationally , the needed particle is reduced, and the real-time of algorithm is improved. In this chapter, in the basis of kernel particle filter, the new algorithm could adjust number of particle adaptively, the agility of whole algorithm is better. Experiment result show algorithm could tracking target very well when target is occluded, and algorithm is more real-time comparing with traditional particle filter
第三章针对Mean Shift算法不能跟踪快速目标的特点,提出Mean Shift和卡尔曼滤波器相结合的算法,卡尔曼滤波器预测目标在本帧的可能位置,Mean Shift算法在该位置邻域内搜索,算法对快速运动的目标的跟踪效果良好,而且对遮挡问题也有很好的处理。
第四章提出Mean Shift的模板更新算法,Mean Shift没有模板更新的能力。模板更新对目标的跟踪至关重要,但没有通用的模板更新算法,一般都是针对某种跟踪算法如何解决模板更新的问题。本文提出一种基于卡尔曼滤波器组的Mean Shift模板更新算法。模板的元素取自目标特征值的概率,通过48个卡尔曼滤波器可以跟踪所有特征值的概率变化。算法构造巧妙,由于使用了较少的卡尔曼滤波器,算法实时性好,鲁棒性更好。
第五章提出核直方图的粒子滤波器目标跟踪算法。算法的系统动态模型具有对速度的学习能力,这样可以减少粒子的维数和所需要的粒子数。观测模型以Mean Shift算法对目标特征值的描述为基础,本文设计了一种新的模板更新算法,更新算法充分利用了粒子滤波器计算的中间值,因而没有增加算法复杂性。模板更新能够使得观测值的获得更可靠,因而提高了算法的鲁棒性。
第六章提出基于Mean Shift粒子滤波器的算法。粒子滤波器算法的主要缺点是需要大量的粒子来近似描述目标的状态,使得算法非常费时。新算法利用Mean Shift算法在重采样之前将粒子收敛到集合靠近目标真实状态的区域内,由于每
Mean Shift is a very good algorithm in visual target tracking area. Many scholars in foreign countries has developed this algorithm in recent years, however, few scholars study it in our country. A few articles can be found last year ever.I experienced many difficulties, when I entered the realm of target tracking. Finally I found the Mean Shift algorithm. I have spent many times in this algorithm, so my dissertation is named Mean Shift. The algorithm has many advantages, for example: very good real-time, robust for occlusion and target distortion, however, it has a few defects. Many improvements are done for these defects. My dissertation is named Mean Shift although, but the content is out the scope of Mean Shift.In third chapter, an algorithm combining kalman filter and Mean Shift is bring forward, arm at the Mean Shift couldn't tracking the fast moving target. Kalman filter is used to forecast possible position of target, then Mean Shift search the real position near the possible position. The algorithm has good effect to fast moving target, and can deal well to occlution.In fourth chapter, a template update algorithm of Mean Shift is put forward. Template update is very important to target tracking. There isn't general algorithm for it however, many template update is arm to given tracking algorithm. In this chapter, an algorithm of template update of Mean Shift based a group kalman filter is proposed. The element of template is probability of eigenvalue of target. These probability are acquire by a kalman filter group which had 48 kalman filters. The whole algorithm conformation is very skilled. Because the number of kalman filter is lesser, algorithm is real-time, and robust.In fifth chapter, an algorithm based on kernel histogram particle filter proposed. System dynamic model of algorithm can learn the velocity of target, so the dimension of particle is reduced and the required particle is very few. Observation model of algorithm is based on Mean Shift describing the eigenvalue. In this chapter, a new template update algorithm is devised. New algorithm makes the best of middle value of particle filter, so that the
complexity of algorithm is not added. Template update enable to get more credible observation, so improve the robustness of algorithm.In sixth chapter, an algorithm based on Mean Shift particle filter is proposed. The main disadvantage of particle filter is it require so many particles to approximately describing state of target, that the real-time of algorithm is not ensured. New algorithm use Mean Shift algorithm to muster the particle to area of real state after re-sample .Because the particle describe the state of more rationally , the needed particle is reduced, and the real-time of algorithm is improved. In this chapter, in the basis of kernel particle filter, the new algorithm could adjust number of particle adaptively, the agility of whole algorithm is better. Experiment result show algorithm could tracking target very well when target is occluded, and algorithm is more real-time comparing with traditional particle filter
引文
1. Collins, R.T. and A.J. Lipton, Introduction to the special section on video surveillance. IEEE Transactions on Patem Analysis and Machine Intelligence, 2000. 22(8): p. 745～746.
2. Sage, K. and S. Young, Security applications of computer vision. IEEE Aerospace and Electronics Systems Magazine, 1999.14(4): p. 19～29.
3. Meyer, M. and T. Ohmacht, Video surveillance applications using multiple views of a scene. IEEE Aerospace and Electronics Systems Magazine, 1999. 14(3): p. 13～18.
4. Doucet, A., N. Gordon, and V. Krishnamurthy, Particle filter for state estimation of jump Markov linear systems. IEEE Trans, Signal Processing., 2001.49: p. 613～624.
5. Haritaoglu, I., D. Harwood, and Davis. W/sup 4,/: Who? When? Where? What? a real time system for detecting and tracking people, in Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition. 1998.
6. Stringa, E. and C.S. Regazzoni, Real-time video-shot detection for scene surveillance applications. IEEE Transactions on Image Processing, 2000. 9(1): p. 69～79.
7. Bobick, A.F. and A.D. Wilson. A state-based technique for the summarization of recognition of gesture, in Proc. Int'l Conf. Computer Vision. 1995.
8. Darell, T., B. Moghaddam, and A. Pentland. Active face tracking andpose estimation in an Interactive Room. in Proc. IEEE Conf. Computer Vision and Pattern Recognition.1996.
9. Gavrila, D. and L. Davis. Tracking humans in action: a 3D model-based approach, in Proc. Image Understanding Workshop. 1996.
10. Dellaert, F., D. Pomerleau, and C. Thorpe. Model-based car tracking integrated with a road-follower, in Int'l Conf. on Automation. 1998.
11. Taylor, C.J., J. Malik, and J. Weber. A real-time approach to stereopsis and lane-finding. in Proc. IEEE Intelligent Vehicles Symposium. 1996.
12. Coombs, D., et al. Real-time obstacle a dance using centrall flow divergence and peripheral How. in Proc. IEEE Int'l Conf. on Computer Vision. 1995.
13. Davison, A. and D. Murray. Moblie robot localisation using active vision, in Proc. European Conf. on Computer Vision. 1998.
14. Hutchinson, S., G. Hager, D,, and P. Corker, A Tutorial introduction to visual servo control. IEEE Trans. Robotics and Automataion 1996. 12(5): p. 651～670.
15. Papanikolopoulos, N., P. Khosla, and T. Kanade, Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision. IEEE Trans. Robotics and Automation, 1993.9(1): p. 14～35.
16. Harrell, R.C., D.C. Slaughter, and P.D. Adsit, A fruit-tracking system for robotic harvesting. Macne Vision and Applications, 1989.2: p. 69～80.
17. Reynard, D., et al. Learing dynamics of complex motions from image sequences, in Proc. European Conf. Computer Vision. 1996.
18. Bardinet, E., L. Cohen, and N. Ayache. Tracking medical 3D data with a deformable parametric model in Proc. European Conf. Computer Vision. 1996.
19. Shi, P., et al. A model-based integrated approach to track myocardial deformation using displacement and velocity constraints, in Proc. int'l Conf Computer Vision. 1995.
20. Boyer, E., Object models from contour sequences. Proc. European Conf. Computer Vision, 1996. 2: p. 109～118.
21. Shapiro, L., Affine analysis of image sequences. Vol. 2. 1995, Cambridge, England. 109～118.
22. Tomasi, C. and T. Kanade, Shape and motion form image streams under orthography: A fractorization method. Int'l J. Computer Vision, 1992.9(2): p. 137～154.
23. Smith, E.A. and D.R. Phillips, Automated cloud tracking using precisely aligned digital ats pictures. IEEE Trans. Computers C-21 (7), 1972.21(7): p. 715～729.
24. Pahlavan, K., Active Robot Vision and Primary Ocular Processes, Ph.D. dissertation. Vol. ISRN KTH/NA/P--93/16--SE. 1993: Dept. of Numerical Analysis and Computing Science, KTH, Stockholm, Sweden.
25. Uhlin, T., et al. Towards an active visual observer, in Proc. 5th International Conference on Computer Vision. 1995. Cambridge, MA.
26. Thompson, W.B., P. Lechleider, and E.R. Stuck, Detecting moving objects using the rigidity constraint. IEEE Trans. Pattern Analysis and Machine Intell, 1993. 15(2): p. 162～165.
27. Meyer, F.G. and P. Bouthemy. Region-based tracking using affine motion models in long image sequences, in CVGIP: Image Understanding. 1994.
28.Murat Tekalp,A.,数字视频处理,ed.崔之枯,江春,陈丽鑫.1998:北京电子工业出版社.
29. Fan, J., J. Yu, and G. Fujita, Spatio-temporal segmentation for compact video representation. Signal Process: Image Communication, 2001.16(553～566).
30. Eckehard, S., E. Peter, and G. Bernd, Motion-BasedAnalysis and Segmentation of Image Sequence Using 3-D Scene Models. Signal Processing, 1998.66(2): p. 233～247.
31. Thomas, M. and K.N. Ngan, Automatic Segmentation of Moving Objects for Video Object Plane Generation. IEEE Transactions on Circuits and Systems for Video Technology, 1998.8(5): p. 525～538.
32. Chuang, G. and L.M. chieh, Semiautomatic Segmentation and Tracking of Semantic Video Objects. IEEE Transactions on Circuits and Systems for Video Technology, 1998. 8(5): p. 572～584.
33. Aggarawal, J.K. and N. Nandhakumar, On the computation of motion from sequences of images. Proceedings of the IEEE, 1988.76: p. 917～935.
34. Smith, S.M. and J.M. Brady, SUSAN--a new approach to low level image processing. Int. Journal of Computer Vision, 1997.23(1): p. 45～78.
35. Odobez, J.M. and P. Bouthemy Direct Incremental Model-Based lmage Motion Segmentation for Video Analysis. Signal Processing, 1998. 66: p. 143～155.
36. Patras, L., E.A. Hendriks, and R.L. Lagendijk Video Segmentation by MAP Labeling of Watershed Segments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001.23(3): p. 326～332.
37. Markandey, V., A. Reid, and S. wang, Motion estimation for moving target detection. IEEE Transaction on Aerospace and Electronic Systems., 1996. 32(3): p. 866～874.
38. Andersson, M., Tracking Methods in Computer Vision, in Dept. of Numerical Analysis and Computing Science, , . 1994, KTH: Stockholm, Sweden. p. ISRN KTH/NA/P--94/13--SE.
39. Zhang, Z., Token tracking in a cluttered scene. Image and Vision Computing, 1994. 12(2): p.110～120.
40. Isard, M. and A. Blake, Condensation-conditional density propagation for visual tracking. International Journal on Computer Vision, 1998. 29(1): p.5～28.
41. Uhlin, T., et al. Towards an active visual observer, in Proc. 5th International Conference on Computer Vision. 1995. Cambridge, MA.
42. Lu, S., et al. Using multiple cues for hand tracking and model refinement, in Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference 2003.
43. Koller, D., K. Daniilidis, and H. -H. Nagel, Model-based object tracking in monocular image sequences of road traffic scenes. Int. J. of Computer Vision, 1993. 10(3): p.257～281.
44. Bregler, C. and J. Malik. Tracking people with twists and exponential maps. in Proc. IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition. 1998.
45. Kakadiaris, L. and D. Metaxas, Model-based estimation of 3D human motion. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2000. 22(12).
46. Haritaoglu, I., D. Harwood, and L. S. Davis. Ghost: A human body part labeling system using silhouettes, in International Conference on Pattern Recognition. 1998. Brisbane.
47. Matsumoto, Y., et al. View-based approach to robot navigation. in Intelligent Robots and Systems, 2000. (IROS 2000). Proceedings. 2000.
48. Ito, K. and S. Sakane. Robust view-based visual tracking with detection of occlusions, in Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference. 2001.
49. Turk, M. and A. Pentland. Face recognition using eigenfaces, in Proc. IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition,. 1991. Maui.
50. Belhumeur, P. N. and D. J. Kriegman. What is the set of images of an object under all possible lighting conditions? in Proc. IEEE Comp. Soc. Conf on Computer Vision and Pattern Recognition. 1996.
51. Zhujie and Y. L. Yu. Face recognition with eigenfaces, in Industrial Technology, 1994. Proceedings of the IEEE International Conference 1994.
52. Black, M. J. and A. D. Jepson, Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. Int. J. of Computer Vision, 1998. 26(1): p.63～84.
53. Cui, Y. and J. J. Weng. View-based hand segmentation and handsequence recognition with complex backgrounds. 1996.
54. Blake. Affine-invariant contour tracking with automatic control of spatiotemporal scale. in Proc. 4th Int. Conf on Computer Vision, IEEE Computer Society Press. 1993. Berlin, Germany.
55. Curwen. Parallel implementation of Lagrangian dynamics for real-time snakes, in Proc. British Machine Vision Conference, Springer Verlag. 1991. New York.
56. Cipolla, R. and A. Pentland, Computer vision for human-computer interaction. 1998, Cambridge, U. K: Cambridge University Press.
57. Shapiro, L. S., H. Wang, and J. M. Brady. A matching and tracking strategy for independently moving objects, in Proc. British Machine Vision Conference, Springer Verlag,. 1992. New York.
58. McFarlane, N. J. B. and C. P. Schofield, Segmentation and tracking of piglets in images. Machine Vision and Applications, 1995. 8: p.187～193.
59. Rasmussen, C., K. Toyama, and G. D. Hager. Tracking objects by color alone. in Technical report, Dept of Computer Science. 1997. Yale University.
60. Wren, C. R., et al., Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Analysis and Machine Intell, 1997.19(7): p.780～785.
61. Lipton. and H. Fujiyoshi. Moving target classification and tracking from real-time video. in Proc. IEEE Workshp Applications of Computer Vision. 1998.
62. Foresti, G. L., Object recognition and tracking for remote video survellance. IEEE Transactions on Circuits and Systems for Video Technology, 1999.9(7): p.1045～1062.
63. Collins, R., R. Gross, and J. Shi, Silhouette-based human identification from body shape and Gait. 5th International Conference on Automatic Face and Guesture Recognition, 2002.
64. Horn, B. K. P., Robot Vision. 1986, New York: McGraw-Hill.
65. Wren, C. R. e., Pfinder: Real-time Tracking of the Human Body. IEEE Trans. On Patern Analysis and Machine Intelligence, 1997. 19(7): p.780～785.
66. Koller and J. Weber. Towards Robust Automatic Traffic Scene Analysis in Real-time. in Proceedings of Int'l Conf. Patern Recognitio. 1994.
67. Stauffer, C., W. Eric, and L. Grimson Learning Patterns of Activity Using Real-timeTracking. IEEE Trans. On Pattern Analysis, 2000.22(8).
68. Toyama, K., et al. Wallflower: Principles and Practice of Background Maintenance. in Inter Conf. on Computer Vision. 1999. Corfu, Greece.
69. Grimson, W. E. L. Using adaptive Tracking to Classify and Monitor Activies in a Site. in IEEE Conference on Computer Vision and Pattern Recognition. 1998. santa Barbara, CA.
70. Nakai, H. Non-Parameterized Bayes Decision Method for Moving Object Detection. in Asian Conf. Computer Vision. 1995. Singapore.
71. Ridder, C., O. Munkelt, and H. Kirchner. Adaptive Background Estimation and Foreground Detection using Kalman-Filtering. in Proceedings of International Conf. on Recent Advances in Mechatronics. 1995. Istanbul, Turkey.
72. Fukunage, K. and L. D. Hostetler, The estimation of the gradient of a density function with application in pattern recognition. IEEE Trans. Information Theory, 1975.21(1): p. 32～40.
73. Cheng, Y., Mean Shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995.17(8).
74. Comaniciu, D. and P. Meer. Mean Shift analysis and application, in Proceedings of the Seventh IEEE International Conference. Computer Vison. 1999.
75. Comaniciu, D. and P. Meer, Mean Shift: A Robust application toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(5): p. 603～619.
76. Comaniciu, D. and P. Meer. Robust Analysis of Feature Spaces: Color Image Segmentation. in Proc. 1997 IEEE conf. Computer Vision and Pattern Recognition. 1997.
77. Comaniciu, D., V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using Mean Shift. in Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference 2000.
78. Comaniciu, D., V. Ramesh, and P. Meer, Kernel-based object tracking. IEEE Transactions. on Pattern Analysis and Machine Intelligence, 2003. 25(2): p.564～577.
79. Comaniciu, D., V. Ramesh, and P. Meer. The variable bandwidth Mean Shift and data-driven scale selection, in In Proc. 8th Intl. Conf on Computer Vision. 2001. Vancouver, Canada.
80. Yang, C., R. Duraiswami, and L. Davis. Similarity Measure for Nonparametric Kernel Density Based Object Tracking. in submitted to NIPS 2004. 2004.
81. Collins, R. T. Mean-Shift Blob Tracking through Scale Space. in 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2003.
82. Nummiaro, K., E. Koller-Meier, and L. Van Goo, Color Features for Tracking Non-Rigid Objects. Chinese Journal of Automation, 2003. 29(3): p.345～355.
83. Kailath, T., The Divergence and Bhattacharyya Distance Measures in Signal Selection. IEEE Trans. Comm. Thchnology., 1967.15: p.52～450.
84. Swain, M. J. and D. H. Ballard, Color Indexing. Intern. J. Comp. Vis., 1991. 7(1): p.11～32.
85. Maggio, E. and A. Cavallaro. Hybrid Particle Filter and Mean Shift tracker with adaptive transition model, in Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP'05). 2005.
86. Nguyen, H. T., M. Worring, and R. v. d. Boomgaard. Occlusion robust adaptive template tracking., in Computer Vision, 2001. ICCV 2001. Proceedings. 2001.
87. NingSong, Peng. Mean shift blob tracking with kernel histogram filtering and hypothesis testing. PRL, 2005: p605～614.
88. 刘富强,数字视频监控系统的开发及应用.2003:机械工业出版社.
89. Liu, J. S. and R. Chen, Sequential Monte Carlo methods for dynamical systems. J, Amer, Statist. Assoc., 1998. 93: p.1032～1044.
90. Gordon, N. and D. Salmond, Bayesian state estimation for tracking and guidance using the bootstrap filter. Journal of Guidance, Control and Dynamics, 1995. 18(6): p.1434～1443.
91. Kitagawa, G., Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics, 1996. 5(1): p.1～25.
92. Arulampalam, M. S., et al., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Transactions on Signal Processing, 2002. 50(2).
93. Heap, T. and D. Hogg, Wormholes in shape space: Tracking through discontinous changes in shape. International Journal on Computer Vision, 1998: p.344～349.
94. MaeCormick, J. and A. Blake, A Probabilistic exclusion principle for tracking multiple objects. International Journal on Computer Vision, 1999. 1: p.572～587.
95. Crisan, D. and A. Doucet, A Survey of convergence results on particle filtering methods for practitioners. IEEE Trans. Speech and Audio Proc., 2002. 10(3): p.173～185.
96.盛骤等,概率论与数理统计.1989,北京:高等教育出版社.
97. Ho, Y. C. and R. C. K. Lee, A Bayesian approach to problem in stochastic estimation and control. IEEE Trans, Automat. Contr., 1964. AC-9: p.333～339.
98.高惠璇,统计计算.1995:北京大学出版社.
99.裴鹿成and王仲奇,蒙待卡罗方法及其应用.1998,北京:北京海洋出版社.
100.裴鹿成and张孝泽,蒙特卡罗方法及其在粒子输运问题中的应用.1980,北京:北京科学出版社.
101. Robert, C. and G. Casella, Monte Carlo statistical methods. 1999: Springer-Verlag. 80.
102. AS Doucet, SS Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 2000. 10(3): p.197～208
103. Djuric, P. M., et al., Particle filtering. Signal Processing Magazine, IEEE, 2003. 20(5): p.19～38.
104. Liu, J. S. and R. Chen, Sequential Monte Carlo Methods for Dynamic Systems. Journal of the American Statistical Association, 1998.
105. Liu, J. S., Monte carlo strategies in scientific computing. 2001: Springer.
106. Doucet, A. and S. Godsill, On sequential Monte Carlo sampling methods for Bayesian filtering. Statist. Computer., 2000. 10: p.197～208.
107. Doucet, A., On sequential Monte Carlo methods for Bayesian filtering. Dept, Eng., Univ. Cambridge, UK, Tech. Rep., 1998.
108. Del Moral, P., Measure valued processes and inteacing particle systems. Ann. Appl. Probab., 1998. 8(2): p.438～495.
109. Kitagawa, G, Meont Carlo filter and smoother for non-Gaussian non-linear state space models. J, Comput. Graph. statis., 1996. 5(1): p.1～25.
110. Carlin, B. P., N. G. Plson, and D. S. Stoffer, A Monte Carlo approach to nonnormal and nonlinear state-space modeling. J, Amer, Statist. Assoc., 1992. 87(418): p.493～500.
111. Godsill, S., A. doucet, and M. West. Methodology for Monte Carlo smoothing with application to time-varying autoregressions, in Proc. Int. Symp. Frontiers Time Series Modeling. 2000.
112. Satoh, Y., T. Okatani, and K. Dguchi. A Color-base Tracking by Kalman Particle Filter. in Proceedings of the 17th International Conference on Pattern Recognition(ICPR'04). 2004. Yoshi, Japan.
113. Dornaika, F. and F. Davoine. Simultaneous facial action tracking and expression recognition using a particle filter,, in IEEE ICCV - International Conference on Computer Vision. 2005. Beijing, China.
114. Zhou, S., R. Chellappa, and B. Moghaddam, Appearance tracking using adaptive models in a particle filter. Proc. of Asian Conf. on Computer Vision 2002.
115. Silverman, B. W., Density Estimation for Statistics and Data Analysis. 1986, New York Chapman and Hall.
116. Bruno, M. G. S., sequential importance sampling filtering for target tracking in image sequences. Signal Processing Letters, IEEE, 2003. 10(8): p.246～249.
117. Shan, C. S., Y. C. Wei, and T. Tan. Real time tracking by combing particle fiter and Mean Shift. in Proceeding of Sixth IEEE International Conference on Automatic Face and Gesture Recognition. 2004.
2. Sage, K. and S. Young, Security applications of computer vision. IEEE Aerospace and Electronics Systems Magazine, 1999.14(4): p. 19～29.
3. Meyer, M. and T. Ohmacht, Video surveillance applications using multiple views of a scene. IEEE Aerospace and Electronics Systems Magazine, 1999. 14(3): p. 13～18.
4. Doucet, A., N. Gordon, and V. Krishnamurthy, Particle filter for state estimation of jump Markov linear systems. IEEE Trans, Signal Processing., 2001.49: p. 613～624.
5. Haritaoglu, I., D. Harwood, and Davis. W/sup 4,/: Who? When? Where? What? a real time system for detecting and tracking people, in Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition. 1998.
6. Stringa, E. and C.S. Regazzoni, Real-time video-shot detection for scene surveillance applications. IEEE Transactions on Image Processing, 2000. 9(1): p. 69～79.
7. Bobick, A.F. and A.D. Wilson. A state-based technique for the summarization of recognition of gesture, in Proc. Int'l Conf. Computer Vision. 1995.
8. Darell, T., B. Moghaddam, and A. Pentland. Active face tracking andpose estimation in an Interactive Room. in Proc. IEEE Conf. Computer Vision and Pattern Recognition.1996.
9. Gavrila, D. and L. Davis. Tracking humans in action: a 3D model-based approach, in Proc. Image Understanding Workshop. 1996.
10. Dellaert, F., D. Pomerleau, and C. Thorpe. Model-based car tracking integrated with a road-follower, in Int'l Conf. on Automation. 1998.
11. Taylor, C.J., J. Malik, and J. Weber. A real-time approach to stereopsis and lane-finding. in Proc. IEEE Intelligent Vehicles Symposium. 1996.
12. Coombs, D., et al. Real-time obstacle a dance using centrall flow divergence and peripheral How. in Proc. IEEE Int'l Conf. on Computer Vision. 1995.
13. Davison, A. and D. Murray. Moblie robot localisation using active vision, in Proc. European Conf. on Computer Vision. 1998.
14. Hutchinson, S., G. Hager, D,, and P. Corker, A Tutorial introduction to visual servo control. IEEE Trans. Robotics and Automataion 1996. 12(5): p. 651～670.
15. Papanikolopoulos, N., P. Khosla, and T. Kanade, Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision. IEEE Trans. Robotics and Automation, 1993.9(1): p. 14～35.
16. Harrell, R.C., D.C. Slaughter, and P.D. Adsit, A fruit-tracking system for robotic harvesting. Macne Vision and Applications, 1989.2: p. 69～80.
17. Reynard, D., et al. Learing dynamics of complex motions from image sequences, in Proc. European Conf. Computer Vision. 1996.
18. Bardinet, E., L. Cohen, and N. Ayache. Tracking medical 3D data with a deformable parametric model in Proc. European Conf. Computer Vision. 1996.
19. Shi, P., et al. A model-based integrated approach to track myocardial deformation using displacement and velocity constraints, in Proc. int'l Conf Computer Vision. 1995.
20. Boyer, E., Object models from contour sequences. Proc. European Conf. Computer Vision, 1996. 2: p. 109～118.
21. Shapiro, L., Affine analysis of image sequences. Vol. 2. 1995, Cambridge, England. 109～118.
22. Tomasi, C. and T. Kanade, Shape and motion form image streams under orthography: A fractorization method. Int'l J. Computer Vision, 1992.9(2): p. 137～154.
23. Smith, E.A. and D.R. Phillips, Automated cloud tracking using precisely aligned digital ats pictures. IEEE Trans. Computers C-21 (7), 1972.21(7): p. 715～729.
24. Pahlavan, K., Active Robot Vision and Primary Ocular Processes, Ph.D. dissertation. Vol. ISRN KTH/NA/P--93/16--SE. 1993: Dept. of Numerical Analysis and Computing Science, KTH, Stockholm, Sweden.
25. Uhlin, T., et al. Towards an active visual observer, in Proc. 5th International Conference on Computer Vision. 1995. Cambridge, MA.
26. Thompson, W.B., P. Lechleider, and E.R. Stuck, Detecting moving objects using the rigidity constraint. IEEE Trans. Pattern Analysis and Machine Intell, 1993. 15(2): p. 162～165.
27. Meyer, F.G. and P. Bouthemy. Region-based tracking using affine motion models in long image sequences, in CVGIP: Image Understanding. 1994.
28.Murat Tekalp,A.,数字视频处理,ed.崔之枯,江春,陈丽鑫.1998:北京电子工业出版社.
29. Fan, J., J. Yu, and G. Fujita, Spatio-temporal segmentation for compact video representation. Signal Process: Image Communication, 2001.16(553～566).
30. Eckehard, S., E. Peter, and G. Bernd, Motion-BasedAnalysis and Segmentation of Image Sequence Using 3-D Scene Models. Signal Processing, 1998.66(2): p. 233～247.
31. Thomas, M. and K.N. Ngan, Automatic Segmentation of Moving Objects for Video Object Plane Generation. IEEE Transactions on Circuits and Systems for Video Technology, 1998.8(5): p. 525～538.
32. Chuang, G. and L.M. chieh, Semiautomatic Segmentation and Tracking of Semantic Video Objects. IEEE Transactions on Circuits and Systems for Video Technology, 1998. 8(5): p. 572～584.
33. Aggarawal, J.K. and N. Nandhakumar, On the computation of motion from sequences of images. Proceedings of the IEEE, 1988.76: p. 917～935.
34. Smith, S.M. and J.M. Brady, SUSAN--a new approach to low level image processing. Int. Journal of Computer Vision, 1997.23(1): p. 45～78.
35. Odobez, J.M. and P. Bouthemy Direct Incremental Model-Based lmage Motion Segmentation for Video Analysis. Signal Processing, 1998. 66: p. 143～155.
36. Patras, L., E.A. Hendriks, and R.L. Lagendijk Video Segmentation by MAP Labeling of Watershed Segments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001.23(3): p. 326～332.
37. Markandey, V., A. Reid, and S. wang, Motion estimation for moving target detection. IEEE Transaction on Aerospace and Electronic Systems., 1996. 32(3): p. 866～874.
38. Andersson, M., Tracking Methods in Computer Vision, in Dept. of Numerical Analysis and Computing Science, , . 1994, KTH: Stockholm, Sweden. p. ISRN KTH/NA/P--94/13--SE.
39. Zhang, Z., Token tracking in a cluttered scene. Image and Vision Computing, 1994. 12(2): p.110～120.
40. Isard, M. and A. Blake, Condensation-conditional density propagation for visual tracking. International Journal on Computer Vision, 1998. 29(1): p.5～28.
41. Uhlin, T., et al. Towards an active visual observer, in Proc. 5th International Conference on Computer Vision. 1995. Cambridge, MA.
42. Lu, S., et al. Using multiple cues for hand tracking and model refinement, in Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference 2003.
43. Koller, D., K. Daniilidis, and H. -H. Nagel, Model-based object tracking in monocular image sequences of road traffic scenes. Int. J. of Computer Vision, 1993. 10(3): p.257～281.
44. Bregler, C. and J. Malik. Tracking people with twists and exponential maps. in Proc. IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition. 1998.
45. Kakadiaris, L. and D. Metaxas, Model-based estimation of 3D human motion. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2000. 22(12).
46. Haritaoglu, I., D. Harwood, and L. S. Davis. Ghost: A human body part labeling system using silhouettes, in International Conference on Pattern Recognition. 1998. Brisbane.
47. Matsumoto, Y., et al. View-based approach to robot navigation. in Intelligent Robots and Systems, 2000. (IROS 2000). Proceedings. 2000.
48. Ito, K. and S. Sakane. Robust view-based visual tracking with detection of occlusions, in Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference. 2001.
49. Turk, M. and A. Pentland. Face recognition using eigenfaces, in Proc. IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition,. 1991. Maui.
50. Belhumeur, P. N. and D. J. Kriegman. What is the set of images of an object under all possible lighting conditions? in Proc. IEEE Comp. Soc. Conf on Computer Vision and Pattern Recognition. 1996.
51. Zhujie and Y. L. Yu. Face recognition with eigenfaces, in Industrial Technology, 1994. Proceedings of the IEEE International Conference 1994.
52. Black, M. J. and A. D. Jepson, Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. Int. J. of Computer Vision, 1998. 26(1): p.63～84.
53. Cui, Y. and J. J. Weng. View-based hand segmentation and handsequence recognition with complex backgrounds. 1996.
54. Blake. Affine-invariant contour tracking with automatic control of spatiotemporal scale. in Proc. 4th Int. Conf on Computer Vision, IEEE Computer Society Press. 1993. Berlin, Germany.
55. Curwen. Parallel implementation of Lagrangian dynamics for real-time snakes, in Proc. British Machine Vision Conference, Springer Verlag. 1991. New York.
56. Cipolla, R. and A. Pentland, Computer vision for human-computer interaction. 1998, Cambridge, U. K: Cambridge University Press.
57. Shapiro, L. S., H. Wang, and J. M. Brady. A matching and tracking strategy for independently moving objects, in Proc. British Machine Vision Conference, Springer Verlag,. 1992. New York.
58. McFarlane, N. J. B. and C. P. Schofield, Segmentation and tracking of piglets in images. Machine Vision and Applications, 1995. 8: p.187～193.
59. Rasmussen, C., K. Toyama, and G. D. Hager. Tracking objects by color alone. in Technical report, Dept of Computer Science. 1997. Yale University.
60. Wren, C. R., et al., Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Analysis and Machine Intell, 1997.19(7): p.780～785.
61. Lipton. and H. Fujiyoshi. Moving target classification and tracking from real-time video. in Proc. IEEE Workshp Applications of Computer Vision. 1998.
62. Foresti, G. L., Object recognition and tracking for remote video survellance. IEEE Transactions on Circuits and Systems for Video Technology, 1999.9(7): p.1045～1062.
63. Collins, R., R. Gross, and J. Shi, Silhouette-based human identification from body shape and Gait. 5th International Conference on Automatic Face and Guesture Recognition, 2002.
64. Horn, B. K. P., Robot Vision. 1986, New York: McGraw-Hill.
65. Wren, C. R. e., Pfinder: Real-time Tracking of the Human Body. IEEE Trans. On Patern Analysis and Machine Intelligence, 1997. 19(7): p.780～785.
66. Koller and J. Weber. Towards Robust Automatic Traffic Scene Analysis in Real-time. in Proceedings of Int'l Conf. Patern Recognitio. 1994.
67. Stauffer, C., W. Eric, and L. Grimson Learning Patterns of Activity Using Real-timeTracking. IEEE Trans. On Pattern Analysis, 2000.22(8).
68. Toyama, K., et al. Wallflower: Principles and Practice of Background Maintenance. in Inter Conf. on Computer Vision. 1999. Corfu, Greece.
69. Grimson, W. E. L. Using adaptive Tracking to Classify and Monitor Activies in a Site. in IEEE Conference on Computer Vision and Pattern Recognition. 1998. santa Barbara, CA.
70. Nakai, H. Non-Parameterized Bayes Decision Method for Moving Object Detection. in Asian Conf. Computer Vision. 1995. Singapore.
71. Ridder, C., O. Munkelt, and H. Kirchner. Adaptive Background Estimation and Foreground Detection using Kalman-Filtering. in Proceedings of International Conf. on Recent Advances in Mechatronics. 1995. Istanbul, Turkey.
72. Fukunage, K. and L. D. Hostetler, The estimation of the gradient of a density function with application in pattern recognition. IEEE Trans. Information Theory, 1975.21(1): p. 32～40.
73. Cheng, Y., Mean Shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995.17(8).
74. Comaniciu, D. and P. Meer. Mean Shift analysis and application, in Proceedings of the Seventh IEEE International Conference. Computer Vison. 1999.
75. Comaniciu, D. and P. Meer, Mean Shift: A Robust application toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(5): p. 603～619.
76. Comaniciu, D. and P. Meer. Robust Analysis of Feature Spaces: Color Image Segmentation. in Proc. 1997 IEEE conf. Computer Vision and Pattern Recognition. 1997.
77. Comaniciu, D., V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using Mean Shift. in Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference 2000.
78. Comaniciu, D., V. Ramesh, and P. Meer, Kernel-based object tracking. IEEE Transactions. on Pattern Analysis and Machine Intelligence, 2003. 25(2): p.564～577.
79. Comaniciu, D., V. Ramesh, and P. Meer. The variable bandwidth Mean Shift and data-driven scale selection, in In Proc. 8th Intl. Conf on Computer Vision. 2001. Vancouver, Canada.
80. Yang, C., R. Duraiswami, and L. Davis. Similarity Measure for Nonparametric Kernel Density Based Object Tracking. in submitted to NIPS 2004. 2004.
81. Collins, R. T. Mean-Shift Blob Tracking through Scale Space. in 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2003.
82. Nummiaro, K., E. Koller-Meier, and L. Van Goo, Color Features for Tracking Non-Rigid Objects. Chinese Journal of Automation, 2003. 29(3): p.345～355.
83. Kailath, T., The Divergence and Bhattacharyya Distance Measures in Signal Selection. IEEE Trans. Comm. Thchnology., 1967.15: p.52～450.
84. Swain, M. J. and D. H. Ballard, Color Indexing. Intern. J. Comp. Vis., 1991. 7(1): p.11～32.
85. Maggio, E. and A. Cavallaro. Hybrid Particle Filter and Mean Shift tracker with adaptive transition model, in Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP'05). 2005.
86. Nguyen, H. T., M. Worring, and R. v. d. Boomgaard. Occlusion robust adaptive template tracking., in Computer Vision, 2001. ICCV 2001. Proceedings. 2001.
87. NingSong, Peng. Mean shift blob tracking with kernel histogram filtering and hypothesis testing. PRL, 2005: p605～614.
88. 刘富强,数字视频监控系统的开发及应用.2003:机械工业出版社.
89. Liu, J. S. and R. Chen, Sequential Monte Carlo methods for dynamical systems. J, Amer, Statist. Assoc., 1998. 93: p.1032～1044.
90. Gordon, N. and D. Salmond, Bayesian state estimation for tracking and guidance using the bootstrap filter. Journal of Guidance, Control and Dynamics, 1995. 18(6): p.1434～1443.
91. Kitagawa, G., Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics, 1996. 5(1): p.1～25.
92. Arulampalam, M. S., et al., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Transactions on Signal Processing, 2002. 50(2).
93. Heap, T. and D. Hogg, Wormholes in shape space: Tracking through discontinous changes in shape. International Journal on Computer Vision, 1998: p.344～349.
94. MaeCormick, J. and A. Blake, A Probabilistic exclusion principle for tracking multiple objects. International Journal on Computer Vision, 1999. 1: p.572～587.
95. Crisan, D. and A. Doucet, A Survey of convergence results on particle filtering methods for practitioners. IEEE Trans. Speech and Audio Proc., 2002. 10(3): p.173～185.
96.盛骤等,概率论与数理统计.1989,北京:高等教育出版社.
97. Ho, Y. C. and R. C. K. Lee, A Bayesian approach to problem in stochastic estimation and control. IEEE Trans, Automat. Contr., 1964. AC-9: p.333～339.
98.高惠璇,统计计算.1995:北京大学出版社.
99.裴鹿成and王仲奇,蒙待卡罗方法及其应用.1998,北京:北京海洋出版社.
100.裴鹿成and张孝泽,蒙特卡罗方法及其在粒子输运问题中的应用.1980,北京:北京科学出版社.
101. Robert, C. and G. Casella, Monte Carlo statistical methods. 1999: Springer-Verlag. 80.
102. AS Doucet, SS Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 2000. 10(3): p.197～208
103. Djuric, P. M., et al., Particle filtering. Signal Processing Magazine, IEEE, 2003. 20(5): p.19～38.
104. Liu, J. S. and R. Chen, Sequential Monte Carlo Methods for Dynamic Systems. Journal of the American Statistical Association, 1998.
105. Liu, J. S., Monte carlo strategies in scientific computing. 2001: Springer.
106. Doucet, A. and S. Godsill, On sequential Monte Carlo sampling methods for Bayesian filtering. Statist. Computer., 2000. 10: p.197～208.
107. Doucet, A., On sequential Monte Carlo methods for Bayesian filtering. Dept, Eng., Univ. Cambridge, UK, Tech. Rep., 1998.
108. Del Moral, P., Measure valued processes and inteacing particle systems. Ann. Appl. Probab., 1998. 8(2): p.438～495.
109. Kitagawa, G, Meont Carlo filter and smoother for non-Gaussian non-linear state space models. J, Comput. Graph. statis., 1996. 5(1): p.1～25.
110. Carlin, B. P., N. G. Plson, and D. S. Stoffer, A Monte Carlo approach to nonnormal and nonlinear state-space modeling. J, Amer, Statist. Assoc., 1992. 87(418): p.493～500.
111. Godsill, S., A. doucet, and M. West. Methodology for Monte Carlo smoothing with application to time-varying autoregressions, in Proc. Int. Symp. Frontiers Time Series Modeling. 2000.
112. Satoh, Y., T. Okatani, and K. Dguchi. A Color-base Tracking by Kalman Particle Filter. in Proceedings of the 17th International Conference on Pattern Recognition(ICPR'04). 2004. Yoshi, Japan.
113. Dornaika, F. and F. Davoine. Simultaneous facial action tracking and expression recognition using a particle filter,, in IEEE ICCV - International Conference on Computer Vision. 2005. Beijing, China.
114. Zhou, S., R. Chellappa, and B. Moghaddam, Appearance tracking using adaptive models in a particle filter. Proc. of Asian Conf. on Computer Vision 2002.
115. Silverman, B. W., Density Estimation for Statistics and Data Analysis. 1986, New York Chapman and Hall.
116. Bruno, M. G. S., sequential importance sampling filtering for target tracking in image sequences. Signal Processing Letters, IEEE, 2003. 10(8): p.246～249.
117. Shan, C. S., Y. C. Wei, and T. Tan. Real time tracking by combing particle fiter and Mean Shift. in Proceeding of Sixth IEEE International Conference on Automatic Face and Gesture Recognition. 2004.