科研成果详情

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
ISSN/eISSN2168-2267
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
DOI10.1109/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
被引频次:197[WOS]   [WOS记录]     [WOS相关记录]
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.
条目包含的文件
条目无相关文件。
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[He,Zhenyu]的文章
[Yi,Shuangyan]的文章
[Cheung,Yiu Ming]的文章
百度学术
百度学术中相似的文章
[He,Zhenyu]的文章
[Yi,Shuangyan]的文章
[Cheung,Yiu Ming]的文章
必应学术
必应学术中相似的文章
[He,Zhenyu]的文章
[Yi,Shuangyan]的文章
[Cheung,Yiu Ming]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。