Mining surveillance video for independent motion detection

Abstract

This paper addresses the special applications of data mining techniques in homeland defense. The problem targeted, which is frequently encountered in military/intelligence surveillance, is to mine a massive surveillance video database automatically collected to retrieve the shots containing independently moving targets. A novel solution to this problem is presented in this paper, which offers a completely qualitative approach to solving for the automatic independent motion detection problem directly from the compressed surveillance video in a faster than real-time mining performance. This approach is based on the linear system consistency analysis, and consequently is called QLS. Since the QLS approach only focuses on what exactly is necessary to compute a solution, it saves the computation to a minimum and achieves the efficacy to the maximum. Evaluations from real data show that QLS delivers effective mining performance at the achieved efficiency.