TRAFFIC VOILATIONS AND SPEED SIGNAL JUMP OVER SPEED NO HELIMET USING CNN

Abstract

TrafficVision AI: Real-Time Traffic Violation Detection Using CNN is an intelligent computer vision-based system designed to automatically detect and monitor common traffic violations in real time. The proposed system uses Artificial Intelligence, Computer Vision, Convolutional Neural Networks (CNN), and image/video processing techniques to identify vehicles, traffic signals, riders, and road-rule violations from live camera footage or recorded videos. The system is designed to detect violations such as over-speeding, signal jumping, and riding without a helmet. Video frames are processed to identify and track vehicles, while CNN-based models classify relevant objects and recognize violation patterns. When a violation is detected, the system can record details such as the violation type, vehicle information, time, and captured evidence for further review. The proposed TrafficVision AI system aims to reduce manual traffic monitoring, improve the efficiency of violation detection, and support safer road management. It can be deployed in smart-city environments, traffic intersections, highways, and other locations requiring automated traffic surveillance. By combining CNN-based object recognition with intelligent traffic analysis, the system provides a scalable solution for automated traffic-rule enforcement and road-safety monitoring.