Carried object detection using ratio histogram and its application to suspicious event analysis

This letter proposes a novel method to detect carried objects from videos and applies it for analysis of suspicious events. First of all, we propose a novel kernel-based tracking method for tracking each foreground object and further obtaining its trajectory. With the trajectory, a novel ratio histogram is then proposed for analyzing the interactions between the carried object and its owner After color re-projection, different carried objects can be then accurately segmented from the background by taking advantages of Gaussian mixture models. After bag detection, an event analyzer is then designed to analyze various suspicious events from the videos. Even though there is no prior knowledge about the bag (such as shape or color), our proposed method still performs well to detect these suspicious events. As we know, due to the uncertainties of the shape and color of the bag, there is no automatic system that can analyze various suspicious events involving bags (such as robbery) without using any manual effort. However, by taking advantages of our proposed ratio histogram, different carried bags can be well segmented from videos and applied for event analysis. Experimental results have proved that the proposed method is robust, accurate, and powerful in carried object detection and suspicious event analysis.