Video Segmentation Algorithm in Virtual Advertisement System

Video object segmentation is a critical first step in virtual advertisement system. In this paper, we present belief propagation based method to increase the accuracy and efficiency of segmentation. The background color models are built as a mixture of a global color model and a per-pixel color model, while foreground color likelihood is modeled by spatially global Gaussian Mixture, and a realistic model of shadow effects is built also. The neighborhood dependencies are built with a MRF prior. Belief propagation is used to compute the marginal probability in MRF, and the video objects are segmented effectively. Experiment results show that the algorithm has the good performance, and has been used in our virtual advertisement system.