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	Object Tracking via Robust Multitask Sparse Representation
	Bai, Yancheng; Tang, Ming
刊名	IEEE SIGNAL PROCESSING LETTERS
	2014-08-01
卷号	21期号:8页码:909-913
关键词	Element-wise sparse regularization joint sparse regularization Sparse representation
英文摘要	Sparse representation has been applied to the object tracking problem. Mining the self-similarities between particles via multitask learning can improve tracking performance. However, some particles may be different from others when they are sampled from a large region. Imposing all particles share the same structure may degrade the results. To overcome this problem, we propose a tracking algorithm based on robust multitask sparse representation (RMTT) in this letter. When we learn the particle representations, we decompose the sparse coefficient matrix into two parts in our algorithm. Joint sparse regularization is imposed on one coefficient matrix while element-wise sparse regularization is imposed on another matrix. The former regularization exploits self-similarities of particles while the later one considers the differences between them. Experiments on the benchmark data show the superior performance over other state-of-art algorithms.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Engineering, Electrical & Electronic
研究领域[WOS]	Engineering
收录类别	SCI
语种	英语
WOS记录号	WOS:000336042100001
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/2975]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
作者单位	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Bai, Yancheng,Tang, Ming. Object Tracking via Robust Multitask Sparse Representation[J]. IEEE SIGNAL PROCESSING LETTERS,2014,21(8):909-913.
APA	Bai, Yancheng,&Tang, Ming.(2014).Object Tracking via Robust Multitask Sparse Representation.IEEE SIGNAL PROCESSING LETTERS,21(8),909-913.
MLA	Bai, Yancheng,et al."Object Tracking via Robust Multitask Sparse Representation".IEEE SIGNAL PROCESSING LETTERS 21.8(2014):909-913.

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