Automated Traffic Violation Detection System

Abstract

The growing number of vehicles and increasing traffic violations necessitate intelligent systems to enhance road safety and law enforcement. This project proposes an automated traffic violation detection system using Python, aimed at identifying key traffic violations such as red signal jumping and wrong lane driving. The system utilizes computer vision techniques to detect vehicles and license plates in pre-captured video frames. Optical Character Recognition (OCR) is used to extract license plate numbers from detected vehicles. By analyzing the position and motion of vehicles relative to traffic signals and road lanes, the system determines whether a violation has occurred. Once identified, all violation details are recorded in a structured database, which can be used to automatically generate reports and send notifications to the RTO (Regional Transport Office) via email. This system reduces manual monitoring, ensures quicker action on violations, and improves traffic rule enforcement through automation.