Abstract
In Australian urban roads, pneumatic tubes are temporarily installed over roads to determine the road usage by vehicles. This is a relatively expensive process and the data cannot be obtained for about two weeks until a manual retrieval of data. In the past, we developed a highly accurate real-time computer vision-based system which relied on back ground subtraction, morphological operations and Gaussian filtering to track centroid of vehicles and accurately determine their speeds and count them. However, in this latest research, we provide our updated system that can determine not only speeds of vehicles but also identifies them including cyclists and pedestrian. This is achieved thorough neural network implementation allowing us to determine their speeds even when they do not follow a straight-line movement. This research utilizes the YOLO family, specifically YOLOv5 for neural network implementation. Such a system is very versatile in determining the variety of traffic in intersections that could not be handled in our previous approach using centroid tracking.
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Premaratne, P., Blacklidge, R., Lee, M. (2022). Vehicle Detection, Classification and Counting on Highways - Accuracy Enhancements. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_33
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DOI: https://doi.org/10.1007/978-3-031-13832-4_33
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