On improving behavior subtraction

With the popularity of monitoring devices, huge amount of surveillance data is generated in every minute. The technique for automatic analysis of monitoring videos is in urgent demand. As an extension of background subtraction, behavior subtraction succeeds in detecting the changes of scenes dynamics instead of its photometric properties. In this paper, we first propose a new algorithm in improving behavior subtraction by maximum likelihood estimate and interval estimate methods. After that we apply the improved approach to the framework of video summarization in which the goal is to condense hours of video data into a few short segments. The compressed video clips allow human to catch their interested information quickly. We finally conduct extensive experiments on real-world surveillance videos. The experimental results demonstrate its superior performance to other state-of-the-art methods.