Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes

Abstract: 

                        Automated visual surveillance systems are attracting extensive interest due to public security. In this paper, we attempt to mine semantic context information including object-specific context information and scene-specific context information (learned from object-specific context information) to build an intelligentsystem with robust object detection, tracking, and classification and abnormal event detection. By means of object-specific context information, a cotrained classifier, which takes advantage of the multiview information of objects and reduces the number of labeling training samples, is learned to classify objects into pedestriansor vehicles with high object classification performance. For each kind of object, we learn its corresponding semantic scenespecific context information: motion pattern, width distribution, paths, and entry/exist points. Based on this information, it is efficient to improve object detection and tracking and abnormal eventdetection. Experimental results demonstrate the effectiveness of our semantic context features formultiple real-world traffic scenes.