Visual target tracking via weighted non-sparse representation and online metric learning
文献类型：会议论文
| 作者 | Duan, Jingdi; Fan BJ(范保杰) ; Cong Y(丛杨)
|
| 出版日期 | 2013 |
| 会议名称 | 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013 |
| 会议日期 | December 12-14, 2013 |
| 会议地点 | Shenzhen, China |
| 关键词 | Biomimetics Graphic methods Robotics Target tracking |
| 页码 | 2691-2695 |
| 通讯作者 | Duan, Jingdi |
| 中文摘要 | In this paper, we propose online metric learning tracking method that consider visual tracking as a similarity measurement problem, and incorporates adaptive metric learning and generative histogram model based on non-sparse linear representation into the target tracking framework. We propose a generative histogram model based on non-sparse linear representation, which make full use of the non-sparse coefficients to discriminate between the target and the background. The similarity metric is adaptively learned online to maximize the margin of the distance between the foreground target and background. A bi-linear graph is defined accordingly to propagate the label of each sample. The model can also self-update using the more confident new samples. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms. © 2013 IEEE. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 3 |
| 会议录 | 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-4799-2744-9 |
| WOS记录号 | WOS:000352739000449 |
| 源URL | [http://ir.sia.cn/handle/173321/14771]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 GB/T 7714 | Duan, Jingdi,Fan BJ,Cong Y. Visual target tracking via weighted non-sparse representation and online metric learning[C]. 见:2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013. Shenzhen, China. December 12-14, 2013. |
入库方式： OAI收割
来源：沈阳自动化研究所
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