A COMPUTER VISION-BASED FRAMEWORK FOR AUTOMATED ROAD ACCIDENT DETECTION AND RAPID EMERGENCY RESPONSE

Abstract

Road accidents are a major cause of fatalities and severe injuries worldwide, often due to delays in accident detection and emergency response. With the increasing deployment of surveillance cameras in urban areas, computer vision and deep learning techniques can be utilized for real-time traffic monitoring. This paper proposes a computer vision-based framework for automated road accident detection using CCTV surveillance systems. The system employs deep learning models such as YOLOv8 for object detection, Mask R-CNN for segmentation, and DeepSORT for object tracking to analyze vehicle movements and identify abnormal events like collisions and sudden stops. Upon detecting a potential accident, the system automatically generates alerts and notifies emergency responders with relevant information. Experimental results demonstrate that the proposed framework achieves high detection accuracy while maintaining real-time performance, enabling faster emergency response and improved road safety.