Human segmentation from video by combining random walks with human shape prior adaption

In this paper, we propose an automatic human segmentation algorithm for video conferencing applications. Since humans are the principal subject in these videos, the proposed framework is based on human shape clues to separate humans from complex background and replace or blur the background for immersive communication. We first detect face position and size, track human boundary across frames, and propagate the segmentation likelihood to the next frame for obtaining the trimap to be used as input to the Random Walk algorithm. In addition, we also include gradient magnitude in edge weight to enhance the Random Walk segmentation results. Finally, we demonstrate experimental results on several image sequences to show the effectiveness and robustness of the proposed method.