  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.
