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Any intelligent traffic monitoring system must be able to detect anomaliessuch as traffic accidents in real time. In this paper, we propose aDecision-Tree - enabled approach powered by Deep Learning for extractinganomalies from traffic cameras while accurately estimating the start and endtime of the anomalous event. Our approach included creating a detection model,followed by anomaly detection and analysis. YOLOv5 served as the foundation forour detection model. The anomaly detection and analysis step entail trafficscene background estimation, road mask extraction, and adaptive thresholding.Candidate anomalies were passed through a decision tree to detect and analyzefinal anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4score of 0.5686, per the experimental validation.

A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees