Object-based coding for long-term archive of surveillance video

This paper describes video coding and segmentation techniques that can be used to achieve significant increase in storage capacity. Specifically, we examine the possibility to use object- based coding for efficient long-term archiving of surveillance video. We consider surveillance systems with many camera sources in which we are required to store several months of video data for each source, thus storage capacity is a major concern. The paper considers several automatic segmentation algorithms. With each algorithm, we analyze the shape coding overhead and implication on overall storage requirements, as well as the effect each algorithm has on the reconstructed quality of frames. Additionally, this paper reviews techniques to dynamically control the temporal rate of objects in the scene and perform bit allocation. Experimental results show that up to 90% savings in storage can be achieved with the proposed method compared to frame-based video coding techniques. The cost for this savings is that the accuracy of the background is compromised; however, we feel that this is satisfactory for the application under consideration.