IOE OpenIR  > 光电探测与信号处理研究室(五室)
Patches-based Markov random field model for multiple object tracking under occlusion
Mingjun Wu; Xianrong Peng; Qiheng Zhang; Rujin Zhao
Source PublicationSignal Processing
Volume90Issue:5Pages:1518-1529
2010
Language英语
Subtype期刊论文
AbstractIn multiple object tracking, it is challenging to maintain the correct tracks of objects in the presence of occlusions. The paper proposes a new method to this problem, building on the patch representation of object appearance. We formulate multiple object tracking as classification tasks which competitively use the appearance models of the interacting objects. To obtain the optimal configuration of classification, a patches-based MAP-MRF decision framework is presented to make a global inference based on local spatial information existing between adjacent patches and the maximum a posteriori solution is evaluated exactly with graph cuts. As a result, accurate object identification is achieved. Extensive experiments on several difficult sequences validate that the proposed method is effective in dealing with multiple object occlusion, and comparative results show that our method outperforms the previous methods. [All rights reserved Elsevier].; In multiple object tracking, it is challenging to maintain the correct tracks of objects in the presence of occlusions. The paper proposes a new method to this problem, building on the patch representation of object appearance. We formulate multiple object tracking as classification tasks which competitively use the appearance models of the interacting objects. To obtain the optimal configuration of classification, a patches-based MAP-MRF decision framework is presented to make a global inference based on local spatial information existing between adjacent patches and the maximum a posteriori solution is evaluated exactly with graph cuts. As a result, accurate object identification is achieved. Extensive experiments on several difficult sequences validate that the proposed method is effective in dealing with multiple object occlusion, and comparative results show that our method outperforms the previous methods. [All rights reserved Elsevier].
Document Type期刊论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/5015
Collection光电探测与信号处理研究室(五室)
Corresponding AuthorMingjun Wu
Affiliation中国科学院光电技术研究所
Recommended Citation
GB/T 7714
Mingjun Wu,Xianrong Peng,Qiheng Zhang,et al. Patches-based Markov random field model for multiple object tracking under occlusion[J]. Signal Processing,2010,90(5):1518-1529.
APA Mingjun Wu,Xianrong Peng,Qiheng Zhang,&Rujin Zhao.(2010).Patches-based Markov random field model for multiple object tracking under occlusion.Signal Processing,90(5),1518-1529.
MLA Mingjun Wu,et al."Patches-based Markov random field model for multiple object tracking under occlusion".Signal Processing 90.5(2010):1518-1529.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Mingjun Wu]'s Articles
[Xianrong Peng]'s Articles
[Qiheng Zhang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Mingjun Wu]'s Articles
[Xianrong Peng]'s Articles
[Qiheng Zhang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Mingjun Wu]'s Articles
[Xianrong Peng]'s Articles
[Qiheng Zhang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.