📄 Abstract
Traffic safety and regulation enforcement remain critical challenges in urban areas. This paper presents an advanced Speed Tracking and Vehicle Detection System leveraging computer vision techniques, specifically OpenCV and YOLO, integrated with a user-friendly Tkinter GUI and MySQL database for efficient data management. The system identifies vehicles, calculates their speed based on frame-to-frame motion, and logs instances of over speeding. A newly added feature enables automatic number plate recognition and email notifications to alert authorities of speed violations. Designed for real-time performance, the system aims to support traffic management authorities by providing an automated, scalable, and adaptable solution.
🏷️ Keywords
📚 How to Cite:
Sangita Bhoyar, Priya Pachpande, Gaurang Punjabi, Riya Gholap, Shweta Lulla, Ankur Asrani , SPEED TRACKING AND VEHICLE DETECTION SYSTEM , Volume 11 , Issue 3, march 2025, EPRA International Journal of Multidisciplinary Research (IJMR) ,