Intelligent Traffic Violation Detection Using Cameras and ML

Problem statement

Manual enforcement of traffic rules at busy junctions is difficult and resource-intensive. Many violations such as not wearing helmets, triple riding, and crossing red lights go unnoticed, leading to accidents and poor road discipline. There is a need for an automatic system that can detect and record such violations using cameras and AI.

Abstract

This project develops a computer vision-based traffic violation detection system. CCTV or roadside cameras capture live video of road junctions. Object detection models such as YOLO or SSD identify vehicles and riders. Additional logic checks for helmets on two-wheeler riders, counts number of riders per bike, and correlates vehicle movement with traffic signal phase to detect red-light jumping. Evidence snapshots and timestamps are stored in a database and can be linked with vehicle registration numbers.

Components required

• High-resolution CCTV / IP camera
• Edge processing unit (GPU PC / Jetson Nano)
• Deep learning framework (PyTorch / TensorFlow)
• Pretrained object detection model (YOLO, SSD etc.)
• Database and backend server
• Web dashboard for viewing violations

Block diagram

Working

The camera provides a continuous video stream of a road junction. Frames are captured at a suitable rate and fed into a deep-learning-based object detection model that identifies vehicles, riders and helmets. For two-wheelers, the system checks whether each detected rider is wearing a helmet and whether the number of riders exceeds allowed limits. By synchronizing with traffic signal status or using virtual stop-line detection, the system also detects vehicles that cross the stop line during a red signal. For each violation, the system stores cropped images, time, lane information and vehicle region to a database for review by authorities.

Applications

• Traffic police enforcement systems
• Smart city integrated command centers
• Road safety analytics and research
• Automated e-challan generation platforms