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Visual target tracking via weighted non-sparse representation and online metric learning
Duan, Jingdi; Fan BJ(范保杰); Cong Y(丛杨)
作者部门机器人学研究室
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
会议录名称2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013
出版者IEEE
出版地New York
2013
页码2691-2695
收录类别EI ; CPCI(ISTP)
EI收录号20141717633124
WOS记录号WOS:000352739000449
产权排序3
ISBN号978-1-4799-2744-9
关键词Biomimetics Graphic Methods Robotics Target Tracking
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.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/14771
专题机器人学研究室
通讯作者Duan, Jingdi
作者单位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
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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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