Towards Scalable Abnormal Behavior Detection in Automated Surveillance

This study presents a scalable automated video surveillance framework that (1) automatically detects the occurrences of abnormal behavior patterns by both pedestrians and vehicles, and (2) directs the focus of the security personnel to the relevant camera view, thereby providing global situational awareness. Powered by deep learning, our methodology can detect both vision and location-based abnormalities, including the events of vandalism, violence, loitering, scouting, and speeding. The proposed framework requires a low initial investment cost and features both real-time detection of various abnormal behaviors and post-crime analysis in scalable form, by enabling wide-area multi-camera networks with person/object re-identification. By combining multiple functionalities in an efficient framework, the proposed system opens up new possibilities for surveillance.