Intelligent Traffic Monitoring and Management Based on Computer Vision Technology

Traditional traffic monitoring systems are limited by the number of devices and the efficiency of location data processing and analysis, making it difficult to achieve timely perception and response to traffic conditions, and cannot adapt to dynamic changes in traffic conditions. By utilizing machine vision technology, it can compensate for the shortcomings in traditional traffic monitoring systems and improve the efficiency of intelligent traffic monitoring and management. By using cameras and other devices to collect image data from roads, preprocessing operations such as image denoising are performed on the collected image data to improve image quality and extract traffic information. This article uses the YOLO (You Only Look Once) object detection algorithm to detect and recognize objects in the collected images, and uses a Kalman filter for vehicle motion analysis. It can predict its direction and speed of travel, display the results of traffic flow and road condition analysis to traffic workers, facilitate relevant personnel to understand the traffic situation, and adjust and modify traffic management strategies. After using computer vision technology to achieve intelligent traffic monitoring and management, the average traffic diversion time was reduced by 48 minutes and the average accident rate was reduced by $0.3 \%$. Intelligent traffic monitoring and management based on computer vision technology can provide functions such as illegal behavior monitoring and traffic flow optimization by processing and analyzing image data in traffic roads, improving the level of traffic management and road traffic safety.