Moving Objects Segmentation from compressed surveillance video based on Motion Estimation

Pixel-domain analysis, the mainstream approach to analyze surveillance video, has always been a hot issue in academy and industry. However, with the increasing volume and resolution of surveillance video, the flexibility and efficiency of fast processing is garnering more significance. 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 compressed domain based scheme is proposed to model background and segment moving objects based on Motion Estimation (ME) in this paper. The main work and achievements are as follows: 1) a background modeling method with Motion Vector (MV) based on ME is applied to the compressed domain; 2) a method of region modification for Moving Objects Segmentation based on ME is proposed. Experimental results show that our approach can realize moving objects extraction in a fast and effective way.