Accident Detection Through CCTV Surveillance

Abstract

Timely accident detection and emergency response play a crucial role in reducing fatalities and mitigating injuries caused by road accidents. Traditional surveillance systems primarily rely on manual monitoring, which is often inefficient in detecting accidents in real time. "Accident Detection through CCTV Camera Surveillance" presents an AI-powered approach to automate accident identification using deep learning techniques. The system utilizes “Mask R-CNN” and “YOLO v8” to analyse live CCTV footage, accurately detecting accidents and differentiating them from regular traffic events. Once an accident is identified, automated alerts are generated and dispatched to emergency services, including ambulance providers, fire departments, and law enforcement agencies. By minimizing human dependency and response delays, this solution enhances the efficiency of emergency management systems. The proposed framework integrates artificial intelligence with real-time surveillance, providing a scalable and cost effective accident detection mechanism. This research aims to improve road safety, optimize emergency response times, and reduce the loss of life due to delayed medical and rescue assistance. The implementation of this system can significantly contribute to the development of smart cities, reinforcing AI's role in intelligent traffic monitoring and public safety enhancement.