Improved mean shift algorithm for multiple occlusion target tracking
Improved mean shift algorithm for multiple occlusion target tracking
复制标题
多遮挡目标跟踪的改进均值平移算法
DOI：
10.1117/1.2969127
复制
发表时间：
2008-08-01
影响因子：
1.3
通讯作者：
Sang, Nong
中科院分区：
文献类型：
--
作者：
Li, Zheng;Gao, Jun;Sang, Nong
多遮挡目标跟踪是视频监控中的一个难题。但在很多情况下，传统的均值漂移跟踪算法不能鲁棒地跟踪遮挡目标。在这项工作中，我们专注于改进的均值漂移跟踪算法建模和跟踪视频监控场景中的各种遮挡目标。对传统的均值漂移跟踪算法提出了两个主要的改进。首先，在确定重叠斑块属于哪个目标后，可以获得每个遮挡目标的非遮挡部分并应用于跟踪算法。其次，迭代估计所有相关的遮挡目标状态，一个接一个，以消除在跟踪过程中的遮挡效应。对比实验结果表明，改进算法能有效地跟踪多个遮挡目标，而传统的均值漂移跟踪算法则不能。
Multiple occlusion target tracking is usually a difficult problem in video surveillance. But in many cases, traditional mean shift tracking algorithms fail to track occlusion targets robustly. In this work, we focus on improving mean shift tracking algorithms to model and track all kinds of occlusion targets in video surveillance scenes. Two primary improvements on traditional mean shift tracking algorithms are proposed. First, after we determine which target the overlapping patches belong to, the nonocclusion part of each occlusion target can be obtained and applied to the tracking algorithm. Second, all the related occlusion target states are iteratively estimated one after another to eliminate the occlusion effects during the tracking process. Furthermore, the contrast experiment results show that the improved algorithm can track multiple occlusion targets, whereas traditional mean shift tracking algorithms fail.