A Bayesian Framework for Foreground Segmentation

This paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. A Bayesian network is presented to model the interactions among three variables, such as foreground segmentation mask, motion segmentation field, and motion vector held. Given two consecutive images, the conditional joint probability density of the three variables is maximized iteratively to simultaneously achieve foreground segmentation and motion segmentation in a mutually beneficial manner. The solution to the optimization problems are obtained by using iterative conditional mode (ICM) and graph cut algorithm. Incorporating motion information with background subtraction technique makes the segmentation perform in a more semantic level and obtain more accurate results. Experimental results for two video sequences are provided to demonstrate the effectiveness of the proposed approach.