An efficient surveillance coding method based on a timely and bit-saving background updating model

Wei Chen, Xianguo Zhang, Yonghong Tian, Tiejun Huang

Abstract: Background modeling is an important pre-processing step for object detection in surveillance video analysis systems. Recently, it has been proved to be useful for high-efficiency surveillance video coding. In existing works, the modeling background frame often needs to be high-quality encoded so as to achieve a large bit-rate saving. However, the high-quality background frame requires lots of bits in the code stream, so it is infeasible to update the background frame too frequently. Therefore, a better bit-allocation method is desirable to facilitate in-time background updating and bit-saving background coding. In this paper, we firstly build up a background updating model from a detailed analysis of results on surveillance video. Following this, we propose a bit-saving and quality-maintaining background frame coding method. In our method, the background frame can be updated more timely, consequently leading to the better coding efficiency. Experimental results show that our method can achieve more than 15% bit-rate decrease compared with three state-of-art methods.