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	Object Tracking Based on the Combination of Learning and Cascade Particle Filter
	Gong, Hanjie ; Li, Cuihua ; Dai, Pingyang ; Xie, Yi ; Li CH(李翠华) ; Xie Y(谢怡)
	2009
关键词	GEODESIC ACTIVE CONTOURS VISUAL TRACKING IMAGE SEQUENCES SELECTION FEATURES MOTION MODELS
英文摘要	Conference Name:IEEE International Conference on Systems, Man and Cybernetics. Conference Address: San Antonio, TX. Time:OCT 11-14, 2009.; The problem of object tracking in dense clutter is a challenge in computer vision. This paper proposes a method for tracking object robustly by combining the online selection of discriminative color features and the offline selection of discriminative Haar features. Furthermore, the cascade particle filter which has four stages of importance sampling is used to fuse two kinds of features efficiently. When the illumination changes dramatically, the Haar features selected offline play a major role. When the object is occluded, or its rotation angle is very large, the color features selected online play a major role. The experimental results show that the proposed method performs well under the conditions of illumination change, occlusion, object scale change and abrupt motion of object or camera.
语种	英语
出处	http://dx.doi.org/10.1109/ICSMC.2009.5346066
出版者	IEEE SYS MAN CYBERN
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86421]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Gong, Hanjie,Li, Cuihua,Dai, Pingyang,et al. Object Tracking Based on the Combination of Learning and Cascade Particle Filter. 2009-01-01.

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