Suspicious object detection and robbery event analysis

This paper proposes a novel method to detect suspicious objects from videos for robbery event analysis. First of all, a background subtraction using a minimum filter is used for detecting foreground objects from videos. Then, a novel kernel-based tracking method is proposed for tracking each moving object and obtaining its trajectory. Then, we propose a novel robbery event analysis system to analyze suspicious object transferring conditions between any two persons. Usually, when a robbery event happens, there should some suspicious object transferring conditions happening between the robbery and the victim. Since there is no prior knowledge about the object's property, it is difficult to automatically analyze the conditions without any manual efforts. To tackle this problem, a novel ratio histogram is then proposed for finding suspicious objects and then accurately analyzing their transferring conditions. After color re-projection, we use Gaussian mixture models to model the suspicious object's visual properties so that it can be very accurately segmented from videos. After analyzing its subsequent speed, different robbery events can be then effectively detected from videos. Experiment results have proved that the proposed method is robust, accurate, and powerful in robbery event detection.