Edge-computing video analytics for real-time traffic monitoring in a smart city

Johan Barthélemy; Nicolas Verstaevel; Hugh Forehead; Pascal Perez

doi:10.3390/s19092048

Edge-computing video analytics for real-time traffic monitoring in a smart city

Johan Barthélemy, Nicolas Verstaevel, Hugh Forehead, Pascal Perez

Résultats de recherche: Contribution à un journal/une revue › Article › Revue par des pairs

63 Téléchargements (Pure)

Résumé

The increasing development of urban centers brings serious challenges for trafficmanagement. 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.

langue originale	Anglais
Numéro d'article	2048
Nombre de pages	23
journal	Sensors
Volume	19
Numéro de publication	9
Les DOIs	https://doi.org/10.3390/s19092048
Etat de la publication	Publié - 1 mai 2019

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

Accès au document

10.3390/s19092048

2019_BarthelemyJ_articleVersion finale publiée, 7,09 MB

Autres fichiers et liens

Link to publication in Scopus

Contient cette citation

@article{0558301bccf44a6b9af53a4de0121e18,

title = "Edge-computing video analytics for real-time traffic monitoring in a smart city",

abstract = "The increasing development of urban centers brings serious challenges for trafficmanagement. 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{\textquoteright}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{\textquoteright} 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.",

keywords = "CCTV, Edge-computing, IoT, Smart city, Traffic monitoring, Video analytic",

author = "Johan Barth{\'e}lemy and Nicolas Verstaevel and Hugh Forehead and Pascal Perez",

note = "Funding Information: Funding: This research was funded by the Smart Cities and Suburb Program (Round One) of the Australian Government, grant number SCS59378. Publisher Copyright: {\textcopyright} 2019 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2019",

month = may,

day = "1",

doi = "10.3390/s19092048",

language = "English",

volume = "19",

journal = "Sensors",

issn = "1424-8220",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "9",

}

TY - JOUR

T1 - Edge-computing video analytics for real-time traffic monitoring in a smart city

AU - Barthélemy, Johan

AU - Verstaevel, Nicolas

AU - Forehead, Hugh

AU - Perez, Pascal

N1 - Funding Information: Funding: This research was funded by the Smart Cities and Suburb Program (Round One) of the Australian Government, grant number SCS59378. Publisher Copyright: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - The increasing development of urban centers brings serious challenges for trafficmanagement. 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.

AB - The increasing development of urban centers brings serious challenges for trafficmanagement. 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.

KW - CCTV

KW - Edge-computing

KW - IoT

KW - Smart city

KW - Traffic monitoring

KW - Video analytic

UR - http://www.scopus.com/inward/record.url?scp=85065670219&partnerID=8YFLogxK

U2 - 10.3390/s19092048

DO - 10.3390/s19092048

M3 - Article

C2 - 31052514

AN - SCOPUS:85065670219

SN - 1424-8220

VL - 19

JO - Sensors

JF - Sensors

IS - 9

M1 - 2048

ER -

Edge-computing video analytics for real-time traffic monitoring in a smart city

Résumé

SDG des Nations Unies

Accès au document

Autres fichiers et liens

Empreinte digitale

Contient cette citation