Multi-posture Human Detection in Video Frames by Motion Contour Matching

In the paper, we proposed a method for moving human detection in video frames by motion contour matching. Firstly, temporal and spatial difference of frames is calculated and contour pixels are extracted by global thresholding as the basic features. Then, skeleton templates with multiple representative postures are built on these features to represent multiposture human contours. In the detection procedure, a dynamic programming algorithm is adopted to find best global match between the built templates and with extracted contour features. Finally a thresholding method is used to classify a matching result into moving human or negatives. And in the matching process scale problem and interpersonal contour difference are considered. Experiments on real video data prove the effectiveness of the proposed method.

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