An Intelligent Road Surveillance and Monitoring System Using Deep Learning-Based Computer Vision

The rapid growth of urban populations and vehicular traffic presents significant challenges for road safety and traffic management. Traditional monitoring methods, which rely on manual observation or limited-capability sensors, are often inefficient, costly, and not scalable. This paper presents an automated, multi-functional road surveillance system that leverages state-of-the-art computer vision and deep learning techniques. The system processes real-time video feeds from standard surveillance cameras to perform a variety of tasks, including vehicle detection and classification, multi-object tracking, speed estimation, and anomaly detection such as illegal maneuvers and stopped vehicles. The core of the system is built upon the YOLO (You Only Look Once) object detector for high-speed performance and the Deep SORT algorithm for robust tracking. By providing real-time data and alerts, this framework offers a cost-effective and intelligent solution to enhance traffic efficiency, improve road safety, and provide valuable data for urban planning and traffic flow analysis.