Human detection in crowded scenes

In this paper, our focus is to segment the foreground area for human detection. It is assumed that the foreground region has been detected. Accurate foreground contours are not required. The developed approach adopts a modified ISM (Implicit Shape Model) to collect some typical local patches of human being and their location information. Individuals are detected by grouping some local patches in the foreground area. The method can get good results in crowded scenes. Some examples based on CAVIAR dataset have been shown. A main contribution of the paper is that ISM model and joint occlusion analysis are combined for individual segmentation. There are mainly two advantages: First, with more sufficient information inside the foreground region, even the individuals inside a dense area can also be handled. Secondly, the method does not require an accurate foreground contour. A rough foreground area can be easily obtained in most situations.

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