In this thesis, we present an automatic and accurate pedestrian segmentation algorithm by incorporating pedestrian shape prior into random walks segmentation from a static image. The Random Walks algorithm requires user-specified labels to produce segmentation with each pixel assigned to a label. This algorithm can provide satisfactory segmentation result with suitable input labeled seeds. Therefore, for taking advantage of this interactive segmentation algorithm, we improve the random walks segmentation algorithm by using prior shape information, which provides appropriate seeds for the pedestrian segmentation from the input image. By using the human shape prior information, we develop a fully automatic pedestrian image segmentation algorithm. The experimental results demonstrate improved segmentation results on some real images by using the proposed algorithm.

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