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Article

Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City

SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2522, Australia
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Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2048; https://doi.org/10.3390/s19092048
Received: 28 February 2019 / Revised: 8 April 2019 / Accepted: 28 April 2019 / Published: 2 May 2019
(This article belongs to the Special Issue Smart IoT Sensing)
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. View Full-Text
Keywords: edge-computing; IoT; smart city; video analytic; traffic monitoring; CCTV edge-computing; IoT; smart city; video analytic; traffic monitoring; CCTV
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MDPI and ACS Style

Barthélemy, J.; Verstaevel, N.; Forehead, H.; Perez, P. Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors 2019, 19, 2048. https://doi.org/10.3390/s19092048

AMA Style

Barthélemy J, Verstaevel N, Forehead H, Perez P. Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors. 2019; 19(9):2048. https://doi.org/10.3390/s19092048

Chicago/Turabian Style

Barthélemy, Johan, Nicolas Verstaevel, Hugh Forehead, and Pascal Perez. 2019. "Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City" Sensors 19, no. 9: 2048. https://doi.org/10.3390/s19092048

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