Intelligent Traffic Surveillance: Machine Learning-Based Detection of Vehicle Speed Violations

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.