Low-complexity and high-efficiency background modeling for surveillance video coding
Recently, background modeling (shortly BgModeling) plays a more and more important role in high-efficiency surveillance video coding. Meanwhile, many practical video coding applications also present some specific requirements for BgModeling, such as the low memory cost and low computational complexity. However, existing BgModeling methods are mostly designed for video content analysis such as object detection. Thus they may be not directly applicable for video coding. In this paper, we firstly present an analysis for the features of BgModeling in surveillance video coding and make a comparison of the performances of existing BgModeling methods. Then we propose a segment-and-weight based running average (SWRA) method for surveillance video coding. SWRA firstly divides pixels at each position in the training frames into several temporal segments, and then calculate their corresponding mean values and weights. After that, a running and weighted average procedure is used to reduce the influence of foreground pixels and finally obtain the modeling results. Experimental results show that, the SWRA-based encoder achieves the best performance over several state-of-the-art methods, with much less cost of memory and modeling time.