A Computer Vision System for Real-Time Vehicular Traffic Management

This paper explores the integration of computer vision technology into a real-time traffic management system. It outlines the development and implementation of a computer vision model designed to optimize traffic flow, detailing technical aspects of the project. The system's backend is developed in Python, utilizing widely used artificial intelligence libraries, enabling administrators to monitor and maintain the system effectively. A key feature of the system is an application that recognizes vehicles based on their license plates and color, cross-referencing this information with a database to detect traffic rule violations such as unpaid fees, fines, or missing vignettes. By combining advanced computer vision techniques with efficient programming frameworks, this project demonstrates a scalable, practical approach to modern traffic management challenges.