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题名: Locality constrained low-rank sparse learning for object tracking
作者: 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: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议录出版者: IEEE
会议录出版地: Piscataway, NJ, USA
出版日期: 2015
页码: 508-513
收录类别: EI ; CPCI(ISTP)
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
产权排序: 1
WOS记录号: WOS:000380502300098
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/18515
Appears in Collections:机器人学研究室_会议论文

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Recommended Citation:
Fan BJ,Tang YD. Locality constrained low-rank sparse learning for object tracking[C]. 见:2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Shenyang, China. June 8-12, 2015.
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文件名: Locality constrained low-rank sparse learning for object tracking.pdf
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