Surveillance video synopsis in the compressed domain for fast video browsing

The traditional pixel-domain based video analysis methods have taken dominated places for long. However, due to the rapidly increasing volume and resolution of surveillance video, the desirable fast and scalable browsing encounters significant challenges in terms of efficiency and flexibility. Under this circumstance, operating surveillance video in compressed domain has aroused great concern in academy and industry. In order to perform the intelligent video analysis task on the premise of preserving accuracy and controlling complexity, this paper presents a compressed-domain approach for massive surveillance video synopsis generation, labeling and browsing. The main work and achievements include: (1) a compressed-domain scheme is established to condense the compressed surveillance video and record the synopsis results; (2) a background modeling method via the Motion Vector based Local Binary Pattern (MVLBP) is introduced to extract moving objects in an efficient way; (3) an object flags based synopsis labeling method is proposed to represent the object regions as well as their display modes in a flexible way. Experimental results show that the video analysis system based on this framework can provide not only efficient synopsis generation but also flexible scalable or playback browsing.