Abstract
Using unmanned aerial vehicles (UAV) as devices for traffic data collection exhibits many advantages in collecting traffic information. This paper introduces a new vehicle dataset based on image data collected by UAV first. Then a novel learning framework for robust on-road vehicle recognition is presented. This framework starts with conventional supervised learning to create initial training data set. Then a tracking-based online learning approach is applied on consecutive frames to improve the accuracy of vehicle recogniser. Experimental results show that the proposed algorithm exhibits high accuracy in vehicle recognition at different UAV altitudes with different view scopes, which can be used in future traffic monitoring and control in metropolitan areas.
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Le, X., Jo, J., Youngbo, S., Stantic, D. (2019). Detection and Classification of Vehicle Types from Moving Backgrounds. In: Kim, JH., et al. Robot Intelligence Technology and Applications 5. RiTA 2017. Advances in Intelligent Systems and Computing, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-78452-6_39
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DOI: https://doi.org/10.1007/978-3-319-78452-6_39
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