A probabilistic framework for segmentation and tracking of multiple non rigid objects for video surveillance
A probabilistic framework for segmentation and tracking of multiple non rigid objects for video surveillance
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DOI：
10.1109/icip.2004.1418763
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发表时间：
2004-10
期刊：
影响因子：
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通讯作者：
A. Ivanovic;Thomas S. Huang
中科院分区：
文献类型：
--
作者：
A. Ivanovic;Thomas S. Huang
本文提出了一个概率框架分割和跟踪多个非刚性前景目标的视频监控，使用静态单目摄像机。该算法结合了概率意义上的信息，并提出了一个非二分匹配问题的匹配分割的前景对象与斑点在下一帧的问题。为了解决这个问题，计算每个可能匹配的概率。也以概率的方式处理新对象的随机性。新的框架被证明能够处理更大的一组困难的情况，并显着提高性能。
This paper presents a probabilistic framework for segmenting and tracking multiple non rigid foreground objects for video surveillance, using a static monocular camera. The algorithm combines information in a probabilistic sense and poses the problem of matching the segmented foreground objects with blobs in the next frame as a non bipartite matching problem. To solve this problem, probability is calculated for each possible matching. Initialization of new objects is also treated in a probabilistic manner. The new framework is shown to be able to handle a greater set of difficult situations and to improve performance significantly.