Anomaly Detection on Traffic Videos Based on Trajectory Simplification

detecting anomalies in the Traffic Control Systems (TCS) could be very useful for the accident analysis, fault detection and other traffic-related topics. In this article we propose a general framework for the trajectory-based anomaly detection, which is fast and reliable. Experimental results show that the system could be used on a vast variety of camera types and configurations. We have used a semi-supervised anomaly detection in the framework which learns from the trajectories of " normal" movements and detects the trajectories that does not fit on the trained model. The trajectories are simplified using a line simplification algorithm to improve the performance while increasing robustness on the noisy inputs.