| Visual target tracking via weighted non-sparse representation and online metric learning | |
Duan, Jingdi; Fan BJ(范保杰); Cong Y(丛杨)
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| Department | 机器人学研究室 |
| Conference Name | 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013 |
| Conference Date | December 12-14, 2013 |
| Conference Place | Shenzhen, China |
| Source Publication | 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013 |
| Publisher | IEEE |
| Publication Place | New York |
| 2013 | |
| Pages | 2691-2695 |
| Indexed By | EI ; CPCI（ISTP） |
| EI Accession number | 20141717633124 |
| WOS ID | WOS:000352739000449 |
| Contribution Rank | 3 |
| ISBN | 978-1-4799-2744-9 |
| Keyword | Biomimetics Graphic Methods Robotics Target Tracking |
| Abstract | 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. |
| Language | 英语 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/14771 |
| Collection | 机器人学研究室 |
| Corresponding Author | Duan, Jingdi |
| Affiliation | 1.Neusoft Corporation, Shenyang 110179, China 2.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210046, China 3.State Key Laboratory of Robotics, Shenyang Institute Automation, Chinese Academy of Sciences, Shenyang, 110016, China |
| Recommended Citation GB/T 7714 | Duan, Jingdi,Fan BJ,Cong Y. Visual target tracking via weighted non-sparse representation and online metric learning[C]. New York:IEEE,2013:2691-2695. |
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| Visual target tracki（219KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Download | ||
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