A Vision-Based System for Traffic Anomaly Detection Using Deep Learning and Decision Trees

Armstrong Aboah; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 4207-4212

Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real-time. In this paper, we propose a Decision-Tree enabled approach powered by Deep Learning for extracting anomalies from traffic cameras while accurately estimating the start and end time of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an s4 score of 0.5686, per the experimental validation.