Automatic foreground extraction in video

Haoqian Wang, Bowen Deng, Kai Li, Yongbing Zhang, Lei Zhang

Abstract: This paper presents an automatic and efficient system for extracting dynamic objects of interest from videos. We take advantage of a saliency map and an optimization-based segmentation algorithm to extract the foreground objects automatically in some key frames. Then, the segmentation results in those key frames are propagated to other frames via an error map-based propagation scheme. Finally, a Bayesian matting-based refinement approach is employed to to handle the topology changes. Experiments show that our system is able to generate high quality results at a low computation cost.