A Video Foreground Segmentation Method for Dynamic Scenes

Video foreground segmentation is one of the key problems in the field of computer vision. It has important value in many applications, such as video surveillance, retrieval and event detection. Traditional video foreground segmentation algorithms are mainly designed for static scene and cannot competent in dynamic scenes. In this article, a novel video foreground segmentation method based on Gaussian mixture model (GMM) and optical flow residual is proposed. Firstly, the preliminary foreground region is estimated by GMM; then, the foreground region with dynamic texture is detected by optical flow residuals and removed; finally, morphology is utilized to refine the estimated foreground. Experimental evaluation shows that the proposed method can obtain more accurate foreground region in dynamic scenes compared with existing methods.