Treffer: Vehicle Speed Detec�on System using OpenCV and Python

Ensuring road safety through effective speed monitoring is a critical aspect of modern traffic management. Traditional methods such as radar guns and fixed speed cameras, though effective, face challenges in cost, flexibility, and scalability. In response, this project introduces SmartVision, a vision-based speed detection framework powered by OpenCV and Python. Unlike static systems, SmartVision employs dynamic vehicle tracking and speed estimation techniques, enabling real-time monitoring across multiple lanes using standard CCTV feeds. The system uses motion detection and centroid tracking to detect and follow vehicles across frames. By leveraging known distances and timestamps, it calculates individual vehicle speeds and flags those exceeding defined limits. The framework is optimized for parallel tracking of multiple vehicles, offering a lightweight yet powerful alternative to conventional setups. With enhancements like object detection models (e.g., YOLO) and edge-device deployment, SmartVision promises a scalable, low-cost solution adaptable to both urban and rural environments. Experimental validation shows that SmartVision delivers reliable performance with high accuracy, paving the way for intelligent traffic enforcement and data-driven smart city development.