Real-time foreground segmentation on GPUs using local online learning and global graph cut optimization

This paper is to address the problem of foreground separation from the background modeling perspective. In particular, we deal with the difficult scenarios where the background texture might change spatially and temporally. A novel approach is proposed that incorporates a pixel-based online learning method to adapt to temporal background changes promptly, together with a graph cuts method to propagate per-pixel evaluation results over nearby pixels. Empirical experiments on a variety of datasets demonstrate the competitiveness of the proposed approach, which is also able to work in real-time on the Graphics Processing Unit (GPU) of programmable graphics cards.