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Locality constrained low-rank sparse learning for object tracking
Fan BJ(范保杰); Tang YD(唐延东)
作者部门机器人学研究室
会议名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议日期June 8-12, 2015
会议地点Shenyang, China
会议录名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
出版者IEEE
出版地Piscataway, NJ, USA
2015
页码508-513
收录类别EI ; CPCI(ISTP)
EI收录号20161402187903
WOS记录号WOS:000380502300098
产权排序1
ISSN号2379-7711
ISBN号978-1-4799-8730-6
关键词Object Tracking Low Rank Sparse Learning Locality Information Collaboration Metric
摘要In this paper, we present a locality constrained low rank sparse learning algorithm for object tracking under the particle filter framework. Locality should be as important as the sparsity. It can further exploit spatial relationship among particles and increase the consistency of low rank coding. Locality information among the training data and dictionary is mined. This can be achieved by using the local constraints as the regularization term. Combined the low rank and sparse criteria, the total objective function is constructed for locality constrained low rank sparse learning. It can be solved by a sequence of closed form update operations. The best target candidate is chosen by jointly evaluating the reconstructive error and classification error. Extensive experimental results on challenging video sequences demonstrate that the proposed tracking method achieves state-of-the-art performance in term of accuracy and 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/173321/18515
专题机器人学研究室
作者单位1.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China
2.State Key Laboratory of Robotics, Shenyang Institute Automation, Chinese Academy of Sciences, Shenyang, China
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Fan BJ,Tang YD. Locality constrained low-rank sparse learning for object tracking[C]. Piscataway, NJ, USA:IEEE,2015:508-513.
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