VISION BASED TRAFFIC CONTROL SYSTEM FOR PEDESTRIAN SAFETY

This research presents a vision-based dynamic traffic signal control system that leverages computer vision and deep learning (YOLOv8) to improve pedestrian safety and traffic efficiency. Unlike traditional fixed-time signals, the system dynamically adjusts pedestrian crossing times based on real-time pedestrian density, ensuring safe crossings while minimizing vehicle delays. It further incorporates automated violation detection, including red-light running, jaywalking, and stop-line violations, supported by license plate recognition for enforcement. With high detection accuracy (95.4%) and near real-time response, the proposed system offers a scalable and intelligent solution for modern urban mobility. Its contribution lies in combining adaptive traffic light optimization with automated violation monitoring, laying the groundwork for integration into future smart city infrastructure.