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Structured low rank tracker with smoothed regularization
Fan BJ(范保杰); Cong Y(丛杨); Li XM(李小毛); Tang YD(唐延东)
作者部门机器人学研究室
会议名称2016 IEEE Visual Communication and Image Processing, VCIP 2016
会议日期November 27-30, 2016
会议地点Chengdu, China
会议录名称VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
出版者IEEE
出版地New York
2016
页码1-4
收录类别EI ; CPCI(ISTP)
EI收录号20170503316893
WOS记录号WOS:000392647500065
产权排序2
ISBN号978-1-5090-5316-2
关键词Object Tracking Structured Low Rank Label Information Smoothed Regularization Iterative Reweighed Least Squares
摘要In this paper, we propose a structured low rank learning algorithm with smoothed regularization for robust object tracking, under particle filter framework. Specifically, the relationships among the particles are exploited with structured low rank regularization term, and simultaneously handle the outlier using a group sparsity regularization. The label information from training data is incorporated into the tracking objective function as the classification error term and idea coding regularization term respectively. By the smoothed regularization, the developed structured low rank learning based tracker can be efficiently solved by iterative reweighed least squares algorithm(IRLS), and avoids svd operation. Moreover, the collaborate normalized metric is developed to find the best candidate. Compared with some state-of-the-art tracking methods on 50 challenging sequences, the proposed algorithms perform well in terms of accuracy, robustness.
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/173321/19877
专题机器人学研究室
通讯作者Fan BJ(范保杰)
作者单位1.Nanjing University of Posts and Telecommunications, Nanjing, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, China
3.School of Mechatronic Engineering and Automaion, Shanghai University, Shanghai, China
推荐引用方式
GB/T 7714
Fan BJ,Cong Y,Li XM,et al. Structured low rank tracker with smoothed regularization[C]. New York:IEEE,2016:1-4.
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