Video Segmentation with Model-Based Sprite Generation for Panning Surveillance Cameras

Video segmentation is one of the most important techniques for advanced video surveillance systems. Most of the existing video segmentation algorithms suffer from high computational complexity to achieve acceptable results for surveillance systems and cannot deal with moving camera situations. In this paper, we propose a low complexity video segmentation algorithm with model-based sprite generation for panning cameras. The characteristics of surveillance systems are utilized to reduce the complexity of sprite generation, which is employed to register background information for panning cameras. In addition, several novel techniques are also proposed to improve the quality of segmentation results. Experimental results show that the proposed algorithm can effectively segment moving objects with good subjective quality.