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Locality constrained low-rank sparse learning for object tracking
Fan BJ(范保杰); Tang YD(唐延东)
Department机器人学研究室
Conference Name2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
Conference DateJune 8-12, 2015
Conference PlaceShenyang, China
Source Publication2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages508-513
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161402187903
WOS IDWOS:000380502300098
Contribution Rank1
ISSN2379-7711
ISBN978-1-4799-8730-6
KeywordObject Tracking Low Rank Sparse Learning Locality Information Collaboration Metric
AbstractIn 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.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/18515
Collection机器人学研究室
Affiliation1.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
Recommended Citation
GB/T 7714
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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