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题名:
Patches-based Markov random field model for multiple object tracking under occlusion
作者: Mingjun Wu; Xianrong Peng; Qiheng Zhang; Rujin Zhao
刊名: Signal Processing
出版日期: 2010
卷号: 90, 期号:5, 页码:1518-1529
通讯作者: Mingjun Wu
文章类型: 期刊论文
中文摘要: 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].
英文摘要: 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].
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/5015
Appears in Collections:光电探测与信号处理研究室(五室)_期刊论文

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作者单位: 中国科学院光电技术研究所

Recommended Citation:
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.
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