Real-time Event Detection and Its Application to Surveillance Systems

Abstract

In recent years, real-time direct detection of events by surveillance systems has attracted a great deal of attention. In this paper, we propose a new video-based surveillance system that can perform real-time event detection. In the background modeling phase, we adopt a mixture of Gaussian approach to determine the background. Meanwhile, we use color blob-based tracking to track foreground objects. Due to the self-occlusion problem, the tracking module is designed as a multi-blob tracking process to obtain similar multiple trajectories. We devise an algorithm to merge these trajectories into a representative one. After applying the Douglas-Peucker algorithm to approximate a trajectory, we can compare two arbitrary trajectories. The above mechanism enables us to conduct real-time event detection if a number of wanted trajectories are pre-stored in a video surveillance system.