Traffic Management System in Smart Cities to Identify the Vehicle and Detect the Speed of Vehicle to Optimize Traffic Management Strategies

Abstract The importance of intelligent vehicle recognition and counting has significantly increased in the context of contemporary highway administration. However, the precision of vehicle counts is directly challenged by the fact that cars vary in size, making it difficult to identify them accurately. This paper introduces a technique for identifying and counting vehicles using vision to tackle this problem. DeepSORT model was used, which is based on the You Only Look Once (YOLO v8) model, to recognize and track vehicles in real time within video sequences. By combining the sophisticated detection capabilities of YOLO v8 with the skilful tracking algorithms of DeepSORT, the suggested method aims to improve accuracy by taking into account the intricacies of various vehicle sizes and movement patterns. The system gives highway management real-time insights into vehicle traffic, enabling them to well-informed information to improve traffic control tactics. Furthermore, the system's capacity to smoothly adjust to intricate traffic situations is enhanced by the combination of YOLO v8 and DeepSORT.