Illumination Robust Video Foreground Prediction Based on Color Recovering

Abstract

Video foreground prediction is a technique to estimate the probability of each pixel being foreground in current frame based on a foreground segmentation result of its previous frame. Existing foreground prediction algorithms usually assume that the illumination conditions are constant for consecutive frames. Therefore, they cannot predict foreground accurately when the illumination condition changes sharply between video frames. In this paper, a new robust video foreground prediction algorithm is proposed based on color recovering, which is derived based on an observation that the illumination changes are locally smooth. By integrating color recovering with an optical flow estimation algorithm and an opacity propagation algorithm, the negative impact of the illumination changes could be removed. Experimental results show that the proposed algorithm can get more accurate results for videos with illumination changes compared with the existing foreground prediction algorithms.