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The paper presents a method for detection of abnormal behaviors related to violent events in urban environments. We have further developed a person detection method and introduced a person tracker as an intermediate stage between person detection and anomaly detection. The method is presented together with results from evaluations based on real sensor data that have been recorded in a realistic urban environment. Several scenarios have been recorded such as normal situations, a person loitering, a person shooting and people escaping from sudden smoke development. The method uses sensor data from a static video surveillance system, consisting of two visual and two thermal infrared cameras. The person detection method combines foreground segmentation with a trained machine learning algorithm which is based on boosting. The tracker associates incoming detections to objects (i.e. people), and generates tracks of world coordinates and velocities of objects. The anomaly detection method, which is based on the hidden Markov model (HMM), fuses information that is derived from the tracker. The information contains a number of persons, their positions and velocities. The results indicate that the person detection method in combination with the tracker can produce robust and accurate observations to the HMM, which in turn provide good conditions for accurate anomaly detection.
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