Human Segmentation Based on Disparity Map and GrabCut

Human segmentation plays an important role in vision analysis due to its importance for applications such as 3D pose estimation, human behavior analysis, body parameter estimation and image compositing. In this paper, we present a new human segmentation method that can segment human from the complex background environment without using background subtraction like algorithms and motion estimation algorithms. Initially, semi-global stereo matching algorithm was used to get the coarse disparity map. Then GrabCut was used on the disparity space image to get the human's coarse silhouette. After some erosion morphology operations, the silhouette obtained from the disparity space image was used as the input marks for the GrabCut, which was used on the color image to get accurate human segmentation. Results show that the proposed method can have a good performance.