| Locality constrained low-rank sparse learning for object tracking | |
Fan BJ(范保杰); Tang YD(唐延东)
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| Department | 机器人学研究室 |
| Conference Name | 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) |
| Conference Date | June 8-12, 2015 |
| Conference Place | Shenyang, China |
| Source Publication | 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) |
| Publisher | IEEE |
| Publication Place | Piscataway, NJ, USA |
| 2015 | |
| Pages | 508-513 |
| Indexed By | EI ; CPCI（ISTP） |
| EI Accession number | 20161402187903 |
| WOS ID | WOS:000380502300098 |
| Contribution Rank | 1 |
| ISSN | 2379-7711 |
| ISBN | 978-1-4799-8730-6 |
| Keyword | Object Tracking Low Rank Sparse Learning Locality Information Collaboration Metric |
| Abstract | 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. |
| Language | 英语 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/18515 |
| Collection | 机器人学研究室 |
| Affiliation | 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 |
| 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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| Locality constrained（942KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Download | ||
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