Unattended Object Identification for Intelligent Surveillance Systems Using Sequence of Dual Background Difference

Abstract

Image-based surveillance systems are widely employed toward safety and security applications in many fields. Cameras, that are connected over an IP network for monitoring public areas, can produce large quantities of video footage. It is tedious for humans to simultaneously observe every type of event on several cameras. Thus, it is necessary to build a user-friendly intelligent system, enabling the analysis of video to detect suspicious events. One of the most important tasks of this system would be to identify unattended objects to prevent an unexpected accident such as the bombing of a public space. This paper presents a novel technique for such a task. The method is based on a sequence of dual background differences, which is obtained by computing the intensity difference between the current and reference background models within a time period. A clustering and an object detector are then integrated to identify the unattended objects. The effectiveness of the method was verified using public and our own databases. The results confirmed that the method is efficient to detect unattended objects and is suitable for implementation in video surveillance systems.