Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model

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	Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model
	Hu, Weiming1 ; Li, Xi 1; Luo, Wenhan 1; Zhang, Xiaoqin 1; Maybank, Stephen 2; Zhang, Zhongfei 3
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2012-12-01
卷号	34 期号:12 页码:2420-2440
文章类型	Article
摘要	Object appearance modeling is crucial for tracking objects, especially in videos captured by nonstationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-euclidean block-division appearance model which captures both the global and local spatial layout information about object appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-euclidean block-division appearance model captures changes in object appearance. For multi-object tracking, the appearance models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.
关键词	Visual Object Tracking Occlusion Reasoning Log-euclidean Riemannian Subspace Incremental Learning Block-division Appearance Model
WOS标题词	Science & Technology ; Technology
关键词[WOS]	VISUAL TRACKING ; PEOPLE ; RECOGNITION ; SCENE
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000309913700011
引用统计	被引频次：107[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3275
专题	多模态人工智能系统全国重点实验室_视频内容安全
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ London Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England 3.SUNY Binghamton, Watson Sch Engn & Appl Sci, Dept Comp Sci, Binghamton, NY 13902 USA
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Hu, Weiming,Li, Xi,Luo, Wenhan,et al. Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2012,34(12):2420-2440.
APA	Hu, Weiming,Li, Xi,Luo, Wenhan,Zhang, Xiaoqin,Maybank, Stephen,&Zhang, Zhongfei.(2012).Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,34(12),2420-2440.
MLA	Hu, Weiming,et al."Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 34.12(2012):2420-2440.