A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees
This work addresses real-time anomaly detection for traffic monitoring systems, but it is incremental as it combines existing deep learning and decision tree methods.
The paper tackled the problem of detecting traffic anomalies like accidents in real-time from camera footage, achieving an F1 score of 0.8571 and an S4 score of 0.5686 in experiments.
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.