Background modeling using Local Binary Patterns Of Motion Vector

Pixel-domain analysis methods are widely adopted in background modeling, some of which are not only concerned by academia but also coming into view of industry. However, as the increasing data volume of video, how to process and analysis videos in a fast and effective way has still been an intractable problem in practical applications. Under this circumstance, surveillance video analysis in the compressed domain is indeed of strategic importance from the angle of balancing visual perception and processing speed, especially in modeling background and segmenting moving objects. Therefore, a background modeling method in the compressed domain is proposed to quickly extract moving objects in this paper. Our main contributions are: 1) a method to calculate MVLBP features based on MV amplitude in the compressed domain is presented; 2) a background modeling and moving objects extraction method is designed in the compressed domain based on Local Binary Patterns of Motion Vector (MVLBP). Experimental results show that our approach gives a stable performance in a shorter time in H.264 compressed domain.