Automatic body segmentation with graph cut and self-adaptive initialization level set (SAILS)

In this paper, we propose an automatic human body segmentation system which mainly consists of human body detection and object segmentation. Firstly, an automatic human body detector is designed to provide hard constraints on the object and background for segmentation. And a coarse-to-fine segmentation strategy is employed to deal with the situation of partly detected object. Secondly, background contrast removal (BCR) and self-adaptive initialization level set (SAILS) are proposed to solve the tough segmentation problems of the high contrast at object boundary and/or similar colors existing in the object and background. Finally, an object updating scheme is proposed to detect and segment new object when it appears in the scene. Experimental results demonstrate that our body segmentation system works very well in the live video and standard sequences with complex background.

Research highlights
► An automatic human body detector is designed for future segmentation.
► A coarse-to-fine strategy is employed to deal with the partly detected object.
► To refine the result, BCR is proposed to remove the contrast in the background.
► SAILS is proposed to solve the boundary problem in region-based segmentation.
► An object updating scheme is proposed for new object when it appears in the scene.