Real-Time Vehicle Accident Recognition from Traffic Video Surveillance using YOLOV8 and OpenCV

The automatic detection of traffic accidents is a significant topic in monitoring systems. It can reduce irresponsible driving behavior, improve emergency response, management, and encourage safer practices. Computer vision be promising technique for accident because it provides reliable, automated, speedy system that response times ultimately save lives. This paper proposed an ensemble model uses the YOLOv8 approach efficient precise event detection. framework's robustness evaluated using YouTube video sequences with various lighting circumstances. has been trained open-source dataset Crash Car Detection Dataset, its produced precision, recall, mAP are 93.8% 98%, 96.1%, respectively, which improvement above prior figures 91.3%, 87.6%, 93.8%. effectiveness real-time surveillance applications proved by experimental results actual data.