基于聚类运动模式的Kullback-Leibler阈值计算方法

Li-na Pan, Zhang Jing, Wang Ping
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引用次数: 0

摘要

为了对密集拥挤场景中的视频运动目标进行聚类,根据视频调节网,通过时空梯度获取每个网格的时空运动模式。利用对称K-L(Kullback-Leibler)散度作为距离度量,可以完成时空运动模式的聚类。聚类的准确性在目标检测中起着重要的作用,K-L阈值是聚类准确性的关键。不同的K-L阈值会导致不同的聚类效果。本文提出了一种基于运动模式的二分类组合功率来准确、快速地确定K-L阈值的方法。
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Kullback-Leibler threshold computing method based on clustering motion patterns
In order to cluster the moving targets of the video in dense crowded scene, according to the video regulation reticulation, it can acquire the spatio-temporal motion patterns of every grid by spatio-temporal gradient. Using the symmetric K-L(Kullback-Leibler) divergence as a distance measure, the clustering of the spatio-temporal motion patterns could be finished. The accuracy of the clustering plays an important role in the target detection, and the K-L threshold is the key for the accuracy of the clustering. Different K-L threshold will lead to different clustering effect. This paper proposes a method based on the dichotomy combining power of the motion pattern to determine the K-L threshold accurately and quickly.
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