Smooth Foreground-Background Segmentation for Video Processing

We propose an efficient way to account for spatial smoothness in foreground-background segmentation of video sequences. Most statistical background modeling techniques regard the pixels in an image as independent and disregard the fundamental concept of smoothness. In contrast, we model smoothness of the foreground and background with a Markov random field, in such a way that it can be globally optimized at video frame rate. As a background model, the mixture-of-Gaussian (MOG) model is adopted and enhanced with several improvements developed for other background models. Experimental results show that the MOG model is still competitive, and that segmentation with the smoothness prior outperforms other methods.