Suspicious object detection using fuzzy-color histogram

This paper proposes a novel method to detect suspicious objects from videos for abnormal event analysis. When considering a robbery event happens, there should be some suspicious object transferring conditions following between the forager 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 ratio histogram based on fuzzy c-means algorithm is proposed for finding suspicious objects. Furthermore, we use Gaussian mixture models to model the suspicious object's visual properties so that it can be accurately segmented from videos. After analyzing its subsequent motion features, different abnormal events like robbery can be effectively detected from videos. Experiment results have proved that the proposed method is robust, accurate, and powerful in abnormal event detection.