Graph-Embedding-Based Learning for Robust Object Tracking

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 视频内容安全

	Graph-Embedding-Based Learning for Robust Object Tracking
	Zhang, Xiaoqin 1,2; Hu, Weiming2 ; Chen, Shengyong 3; Maybank, Steve 4
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
	2014-02-01
卷号	61 期号:2 页码:1072-1084
文章类型	Article
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.
关键词	Graph Embedding Object Tracking Particle Filter Subspace Learning
WOS标题词	Science & Technology ; Technology
关键词[WOS]	VISUAL TRACKING ; MODELS ; SURVEILLANCE ; RECOGNITION ; MOTION ; FILTER ; IMAGE
收录类别	SCI
语种	英语
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000323492000044
引用统计	被引频次：43[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/173211/3270
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.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
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	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.