Safe and Reliable Live-Streaming Traffic Monitoring System using OpenCV and Yolov8

Traffic monitoring is critical for avoiding fatal accidents on the roads as well as essential for ensuring that traffic rules are followed. An effective traffic monitoring system is made possible by cutting-edge computer vision technology, raising the standard of urban traffic safety and lowering the possibility of traffic accidents. The field of urban safety management will benefit greatly from this creative idea. This paper aims to create an automated traffic vehicle monitoring system using an object detection algorithm known as You Only Look Once Version 8 (YOLOv8). The model’s reliability has been enhanced by the use of the YOLOv8 framework for successful vehicle object detection, speed estimation within streaming videos, as well as for license plate recognition. YOLOv8 model is used to identify vehicle objects in every single frame. The model accurately detects vehicles, while the license plate recognition module extracts the text from diverse plate formats. The study’s conclusions are important because they could lead to the development of a real- time, highly accurate, and reasonably priced traffic monitoring system. Road safety might get significantly improved by this research.