Abstract: Urban traffic management is a growing challenge in modern cities, plagued by congestion, accidents, and pollution. The "AI-Powered Traffic Monitoring and Analysis with YOLO" project seeks to address these issues by leveraging advanced computer vision and machine learning technologies. Utilizing the YOLOv8 model, this system provides real-time object detection, accurately identifying various traffic entities such as vehicles and pedestrians. This innovation enhances traffic monitoring capabilities and offers actionable insights for urban planners and authorities, facilitating informed decision-making to improve traffic flow, reduce accidents, and promote safer urban environments. Our approach integrates high-resolution traffic cameras with a central processing unit, leveraging cloud services for data processing and storage. The system's design focuses on scalability, accuracy, and ease of use, ensuring it can adapt to diverse urban environments and integrate seamlessly with existing infrastructure. Through this project, we aim to contribute significantly to the development of smarter, more efficient cities by providing a comprehensive solution to traffic monitoring and management challenges.
Keywords: Machine learning, Linear Regression, Kmeans, analysis, prediction
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DOI:
10.17148/IJARCCE.2024.13906
