Robust Object Tracking via Key Patch Sparse Representation
He, Zhenyu1; Yi, Shuangyan1,2; Cheung, Yiu-Ming3,4; You, Xinge5,6; Tang, Yuan Yan7
Source PublicationIEEE Transactions on Cybernetics

Many conventional computer vision object tracking methods are sensitive to partial occlusion and background clutter. This is because the partial occlusion or little background information may exist in the bounding box, which tends to cause the drift. To this end, in this paper, we propose a robust tracker based on key patch sparse representation (KPSR) to reduce the disturbance of partial occlusion or unavoidable background information. Specifically, KPSR first uses patch sparse representations to get the patch score of each patch. Second, KPSR proposes a selection criterion of key patch to judge the patches within the bounding box and select the key patch according to its location and occlusion case. Third, KPSR designs the corresponding contribution factor for the sampled patches to emphasize the contribution of the selected key patches. Comparing the KPSR with eight other contemporary tracking methods on 13 benchmark video data sets, the experimental results show that the KPSR tracker outperforms classical or state-of-the-art tracking methods in the presence of partial occlusion, background clutter, and illumination change.

KeywordOcclusion Prediction Scheme Particle Filter Patch Sparse Representation Template Update Visual Object Tracking
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Indexed BySCI
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000395476200008
The Source to ArticleWOS
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Cited Times [WOS]:152   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorHe, Zhenyu; Cheung, Yiu-Ming; You, Xinge; Tang, Yuan Yan
Affiliation1.School of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
2.Institute of Research and Continuing Education, Hong Kong Baptist University, Hong Kong.
3.Department of Computer Science and the Institute of Research and Continuing Education, Hong Kong Baptist University (HKBU), Hong Kong
4.United International College, Beijing Normal University—HKBU, Zhuhai 519000, China
5.Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
6.Research Institute of Huazhong University of Science and Technology in Shenzhen, Shenzhen 518057, China
7.Faculty of Science and Technology, University of Macau, Macau 999078, China
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
He, Zhenyu,Yi, Shuangyan,Cheung, Yiu-Ming,et al. Robust Object Tracking via Key Patch Sparse Representation[J]. IEEE Transactions on Cybernetics,2017,47(2):354-364.
APA He, Zhenyu,Yi, Shuangyan,Cheung, Yiu-Ming,You, Xinge,&Tang, Yuan Yan.(2017).Robust Object Tracking via Key Patch Sparse Representation.IEEE Transactions on Cybernetics,47(2),354-364.
MLA He, Zhenyu,et al."Robust Object Tracking via Key Patch Sparse Representation".IEEE Transactions on Cybernetics 47.2(2017):354-364.
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