An Intelligent Traffic Monitoring Embedded System using Video Data Mining
Maria J. P. Peixoto, Akramul Azim, Jim Sheehan, Dan Timothy · 2022
Traffic monitoring through video analysis facilitates understanding the daily driving profile, allowing action to be taken in operational deviations and mitigating the accidents risks or incidents resulting from human error, offering a safer operation for the population. This paper discusses the workflow to create a predictive traffic monitoring system using video data mining on embedded systems. Our proposal uses machine learning, classification models, and tracking techniques to efficiently and accurately extract information about vehicle type, direction, dimensions, and speed through image capture and analysis. The analysis will provide a means to forecast trends using current and historical data and is also intended to be used in conjunction with the NVIDIA JetPack SDK to lower the costs of installing and maintaining sensors. We show that it is possible to classify and track vehicles using artifacts at a lower cost than those already used and without having to interdict the road for sensor structuring.