Graph-Embedding-Based Learning for Robust Object Tracking

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	Graph-Embedding-Based Learning for Robust Object Tracking
	Zhang, Xiaoqin 1,2; Hu, Weiming2 ; Chen, Shengyong 3; Maybank, Steve 4
刊名	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
	2014-02-01
卷号	61 期号:2 页码:1072-1084
关键词	Graph embedding object tracking particle filter subspace learning
英文摘要	Object tracking is viewed as a two-class "one-versus-rest" classification problem, in which the sample distribution of the target over a short period of time is approximately Gaussian while the background samples are often multimodal. Based on these special properties, we propose a graph-embedding-based learning method, in which the topology structures of graphs are carefully designed to reflect the properties of the sample distributions. This method can simultaneously learn the subspace of the target and its local discriminative structure against the background. Moreover, a heuristic negative sample selection scheme is adopted to make the classification more effective. In applications to tracking, the graph-embedding-based learning is incorporated into a Bayesian inference framework cascaded with hierarchical motion estimation, which significantly improves the accuracy and efficiency of the localization. Furthermore, an incremental updating technique for the graphs is developed to capture the changes in both appearance and illumination. Experimental results demonstrate that, compared with the two state-of-the-art methods, the proposed tracking algorithm is more efficient and effective, particularly in dynamically changing and cluttered scenes.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
研究领域[WOS]	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
关键词[WOS]	VISUAL TRACKING ; MODELS ; SURVEILLANCE ; RECOGNITION ; MOTION ; FILTER ; IMAGE
收录类别	SCI
语种	英语
WOS记录号	WOS:000323492000044
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3270]
专题	自动化研究所_模式识别国家重点实验室_视频内容安全团队
作者单位	1.Wenzhou Univ, Inst Intelligent Syst & Decis, Wenzhou 325035, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100864, Peoples R China 3.Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Zhejiang, Peoples R China 4.Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
推荐引用方式 GB/T 7714	Zhang, Xiaoqin,Hu, Weiming,Chen, Shengyong,et al. Graph-Embedding-Based Learning for Robust Object Tracking[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2014,61(2):1072-1084.
APA	Zhang, Xiaoqin,Hu, Weiming,Chen, Shengyong,&Maybank, Steve.(2014).Graph-Embedding-Based Learning for Robust Object Tracking.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,61(2),1072-1084.
MLA	Zhang, Xiaoqin,et al."Graph-Embedding-Based Learning for Robust Object Tracking".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 61.2(2014):1072-1084.