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

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...

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Detalles Bibliográficos
Autores principales: Barthélemy, Johan, Verstaevel, Nicolas, Forehead, Hugh, Perez, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540244/
https://www.ncbi.nlm.nih.gov/pubmed/31052514
http://dx.doi.org/10.3390/s19092048
Descripción
Sumario: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.