With the rapid increase in the number of vehicles on roads, traffic management, and safety enforcement have become significant challenges worldwide. Traditional speed violation detection systems either employ high-end hardware, expensive computational resources, or post-processed video data, which are inefficient to implement in real time. This study presents a real-time intelligent vehicle speed violation detection system using YOLOv8 for object detection and SORT for vehicle tracking, and a new Speed Detection Algorithm (SDA). The system can effectively detect vehicles and calculate their speed from video recorded by low-cost fixed cameras. Unlike other models that process simulated or post-processed video data, the new model processes real-life scenarios such as changing lighting and weather conditions. Experimental results indicate that the system achieves 92% to 95% vehicle detection accuracy while maintaining a Mean Absolute Error (MAE) of 1.8 km/h and Root Mean Square Error (RMSE) of 2.5 km/h for speed estimation, and 98% effective at speed detection compared to various other systems that came before it in terms of real-time processing effectiveness. This cost-effective and scalable solution can be incorporated into traffic observation systems for the improvement of road safety and regulation of speed limit compliance.
Paul et al. (Thu,) studied this question.