题名 | Robust Object Tracking via Key Patch Sparse Representation |
作者 | |
发表日期 | 2017-02-01 |
发表期刊 | IEEE Transactions on Cybernetics
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ISSN/eISSN | 2168-2267 |
卷号 | 47期号:2页码:354-364 |
摘要 | 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. |
关键词 | Occlusion prediction scheme particle filter patch sparse representation template update visual object tracking |
DOI | 10.1109/TCYB.2016.2514714 |
URL | 查看来源 |
语种 | 英语English |
Scopus入藏号 | 2-s2.0-84960539269 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/6372 |
专题 | 北师香港浸会大学 |
作者单位 | 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 |
推荐引用方式 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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