An Effective Method for Foreground Segmentation of Video

In this paper, we propose a novel foreground segmentation approach for applications using static cameras. The foreground segmentation is modeled as an energy function optimum process, where energy function is based on Markov Random Field (MRF) and efficiently optimized by Gibbs sampling. The essence of our method is that we fuse four foreground/background models based on color and texture. This allows composing a robust likelihood term that not only reflects the appearance of foreground/background, but also models the shadow removal process, together with a spatial contrast term and a better temporal persistence term, which achieves a more accurate segmentation. This method has been run on both indoor and outdoor sequences, and the results have proved its effectiveness.