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Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City
Johan Barthélemy1, Nicolas Verstaevel2, Hugh Forehead3
1SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2522, Australia. johan@uow.edu.au.
Abstract:
The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project's aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens' privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments.
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