Traffic Camera Anomaly Detection

Abstract

Detection of camera anomaly and tampering have attracted increasing interest in video surveillance for real-time alert of camera malfunction. However, the anomaly detection for traffic cameras monitoring vehicles and recognizing license plates has not been formally studied and it cannot be solved by existing methods. In this paper, we propose a camera anomaly detection method for traffic scene that has distinct characteristics of dynamics due to traffic flow and traffic crowd, compared with normal surveillance scene. Image quality used as low-level features are measured by no-referenced metrics. Image dynamics used as mid-level features are computed by histogram distribution of optical flow. A two-stage classifier for the detection of anomaly is devised by the modeling of image quality and video dynamics with probabilistic state transition. The proposed approach is robust to many challenging issues in urban surveillance scenarios and has very low false alarm rate. Experiments are conducted on real-world videos recorded in traffic scene including the situations of high traffic flow and severe crowding. Our test results demonstrate that the proposed method is superior to previous methods on both precision rate and false alarm rate for the anomaly detection of traffic cameras.