Conference Proceedings
Identification and classification of trucks and trailers on the road network through deep learning
L Chen, PY Sun, Y Jia, RO Sinnott
BDCAT 2019 - Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies | Association for Computing Machinery | Published : 2019
Abstract
Understanding the flow of traffic on road networks is a primary focus of road transport authorities. A range of technologies has been applied to measure throughput and potential congestion on the roads. However, such technologies are currently limited in disambiguating the kinds of vehicles on the road network. Certain vehicles types are especially important to distinguish from other traffic, e.g. trucks and trailers. These are larger vehicles that can have a major impact on the roads and surrounding areas, e.g. due to pollution or the excess noise that they can give rise to. In this paper, we present an approach to automatically detect, classify and count the unique classes of trucks and tr..
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Awarded by University of Melbourne
Funding Acknowledgements
The authors would like to thank the Roads Cooperation of Victoria (VicRoads) and especially Ben Phillips and Ben Atkinson for providing the original video file to generate the training dataset and the initial idea for the work that has been realised here. The authors also acknowledge the use of the combined HPC/GPU facility (SPARTAN) at the University of Melbourne. This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200.