Object Tracking via Robust Multitask Sparse Representation
文献类型：期刊论文
| 作者 | Bai, Yancheng; Tang, Ming
|
| 刊名 | IEEE SIGNAL PROCESSING LETTERS
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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