부산대학교 도서관

The report focuses on the development and implementation of a real-time traffic analysis system using OpenCV (Open Source Computer Vision Library). The project aims to leverage computer vision techniques to analyze and monitor traffic patterns, providing valuable insights for traffic management and urban planning. The system utilizes video streams from surveillance camerasplaced at strategic locations such as traffic intersections or highways. These video streams are processed in real-time usingOpenCV algorithms to extract relevant information about vehicles, traffic flow, and congestion levels. The extracted data is then analyzed and visualized to provide meaningful insights for traffic management authorities and urban planners. The paper involves several key steps, including video input processing, vehicle detection and tracking, traffic flow analysis, and congestion detection. Each step employs specific OpenCV functionalities and techniques to achieve accurate and efficient results. Throughout the report, we will discuss the various components and methodologies used in developing the system. We will explore the algorithms and techniques employed for vehicle detection and tracking, as well as the methods used to analyze traffic flow and detect congestion. Additionally, we willaddress challenges encountered during the project and present potential future enhancements for the system. The findings of this paper demonstrate the feasibility and effectiveness of using OpenCV-based systems for real-time traffic analysis. The insights gained from this research have the potential to contribute to more efficient traffic management strategies and informed decision-making in urban planning.