Vehicle Direction Detection and Tracking System
A real-time AI-powered vehicle tracking system that detects vehicles, assigns unique IDs, and determines their movement direction (Coming or Going) as they cross a reference line. Built using YOLO and OpenCV, the system is ideal for smart traffic monitoring and surveillance applications.
This project demonstrates a real-time vehicle direction detection and tracking solution using deep learning and computer vision. Vehicles are detected in a video stream using YOLO, tracked across frames with persistent IDs, and classified as “Coming” or “Going” based on their movement across a predefined horizontal reference line. Live counters are displayed directly on the video feed.
This Vehicle Direction Detection and Tracking System combines fast YOLO-based detection with robust motion analysis to deliver real-time directional traffic insights. Its scalable and flexible architecture makes it an excellent foundation for smart traffic automation, urban analytics, and AI-powered surveillance solutions.
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Designed by Mohamed Mohsen