An Integrated Framework for Detection and Geolocation of Traffic Anomalies in Highway Surveillance Videos
Open-source codes of CVEO recent research in intelligent transportation system (ITS) domain, which has been recently accepted for inclusion as an Oral presentation in the ICTTE 2025.
Timely detection and accurate geolocation of traffic anomalies are critical for intelligent highway management and rapid emergency response. This paper presents an intelligent vision-based framework for recognizing and spatially localizing common traffic anomalies, including stopped vehicles, congestion, and pedestrian presence, using roadside non-metric surveillance videos. The framework integrates YOLOX for object detection, SORT for multi-object tracking, and rule-based strategies leveraging spatiotemporal features to identify abnormal events. To enable practical deployment, we develop a geospatial mapping module that transforms pixel-level detections into real-world coordinates via perspective transformation calibrated with sparse point pairs, further enhanced by road centerline integration for directional inference and milepost estimation. Evaluated on a real-world highway dataset, the system achieves promising performance in both event recognition and location accuracy. Experimental results demonstrate the framework’s effectiveness in detecting rare events under imbalanced conditions while providing geospatial information, offering a scalable solution for intelligent transportation systems.
Coming soon.