Assessing the Impact of Video Compression on Background Subtraction

Video cameras are deployed in large numbers for surveillance. Video is compressed and transmitted over bandwidth limited communication channels. Researches have increasingly focused on automated analysis algorithms to understand video. This raises the question “Does compression have an effect on automated analysis algorithms?”. This is a vital question in understanding the reliability of automated analysis algorithms. In this paper, we evaluated background subtraction algorithm’s performance on videos acquired using a typical surveillance camera under different scenarios. The videos in the dataset are collected from an IP-based surveillance camera and compressed using different bitrates and quantization levels. The experimental results provide insights on the different compression settings and their impact on the expected performance of background subtraction algorithms. Furthermore, we train a classifier that utilizes video quality to predict the performance of an algorithm.