Robust Object Tracking via Key Patch Sparse Representation

doi:10.1109/TCYB.2016.2514714

Details of Research Outputs

Title	Robust Object Tracking via Key Patch Sparse Representation
Creator	He，Zhenyu 1; Yi，Shuangyan 1,2; Cheung，Yiu Ming 3,4; You，Xinge 5,6; Tang，Yuan Yan 7
Date Issued	2017-02-01
Source Publication	IEEE Transactions on Cybernetics
ISSN	2168-2267
Volume	47 Issue:2 Pages:354-364
Abstract	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.
Keyword	Occlusion prediction scheme particle filter patch sparse representation template update visual object tracking
DOI	10.1109/TCYB.2016.2514714
URL	View source
Language	英语English
Scopus ID	2-s2.0-84960539269
Citation statistics	Cited Times:197[WOS] [WOS Record] [Related Records in WOS]
Document Type	Journal article
Identifier	http://repository.uic.edu.cn/handle/39GCC9TT/6372
Collection	Beijing Normal-Hong Kong Baptist University
Affiliation	1.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,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,Macao
Recommended Citation GB/T 7714	He，Zhenyu,Yi，Shuangyan,Cheung，Yiu Minget 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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