Object Tracking via Robust Multitask Sparse Representation
Bai, Yancheng; Tang, Ming
发表期刊IEEE SIGNAL PROCESSING LETTERS
2014-08-01
卷号21期号:8页码:909-913
文章类型Article
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.
关键词Element-wise Sparse Regularization Joint Sparse Regularization Sparse Representation
WOS标题词Science & Technology ; Technology
收录类别SCI
语种英语
WOS研究方向Engineering
WOS类目Engineering, Electrical & Electronic
WOS记录号WOS:000336042100001
引用统计
被引频次:24[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/173211/2975
专题多模态人工智能系统全国重点实验室_机器人视觉
作者单位Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China
第一作者单位模式识别国家重点实验室
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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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