Automated Traffic Signal Violation Detection and Real-Time Notification System for Enhanced Road Safety

Traffic violations and accidents are major concerns in urban areas, leading to safety issues and traffic congestion. This research work develops an automated traffic signal detection and notification system to address these problems. The system uses computer vision and machine learning techniques to monitor traffic in real-time through CCTV cameras. It detects vehicles violating traffic signals or involved in accidents by analyzing video frames and applying object detection algorithms. When a violation or accident is detected, the system automatically sends notifications to relevant authorities like nearby police stations and hospitals. Key features include real-time monitoring of traffic through video feeds, detection of signal violations and accidents using computer vision, automated notifications via messaging apps and phone calls, location tracking to alert nearest authorities, and database logging of incidents for future analysis. The system aims to improve road safety and traffic management by enabling quicker response times to incidents. It reduces manual monitoring needs and provides a technological solution to enforce traffic rules. The research demonstrates the potential of AI and IoT technologies in creating smarter and safer transportation systems. Future enhancements could include expanded violation detection and predictive analytics for proactive traffic management.