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Article

Leveraging Edge Intelligence for Video Analytics in Smart City Applications

1
Department of Informatics and Applied Mathematics, Federal University of RN (UFRN), Natal 59078-970, Brazil
2
Digital Metropolis Institute, Federal University of RN (UFRN), Natal 58078-970, Brazil
3
Computer Science Department, Fluminense Federal University (UFF), Niteroi 24220-900, Brazil
*
Author to whom correspondence should be addressed.
Information 2021, 12(1), 14; https://doi.org/10.3390/info12010014
Received: 19 November 2020 / Revised: 8 December 2020 / Accepted: 11 December 2020 / Published: 31 December 2020
(This article belongs to the Special Issue Smart Cyberphysical Systems and Cloud–Edge Engineering)
In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach. View Full-Text
Keywords: video analytics; edge computing; machine learning; edge intelligence; messaging pattern video analytics; edge computing; machine learning; edge intelligence; messaging pattern
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MDPI and ACS Style

Rocha Neto, A.; Silva, T.P.; Batista, T.; Delicato, F.C.; Pires, P.F.; Lopes, F. Leveraging Edge Intelligence for Video Analytics in Smart City Applications. Information 2021, 12, 14. https://doi.org/10.3390/info12010014

AMA Style

Rocha Neto A, Silva TP, Batista T, Delicato FC, Pires PF, Lopes F. Leveraging Edge Intelligence for Video Analytics in Smart City Applications. Information. 2021; 12(1):14. https://doi.org/10.3390/info12010014

Chicago/Turabian Style

Rocha Neto, Aluizio, Thiago P. Silva, Thais Batista, Flávia C. Delicato, Paulo F. Pires, and Frederico Lopes. 2021. "Leveraging Edge Intelligence for Video Analytics in Smart City Applications" Information 12, no. 1: 14. https://doi.org/10.3390/info12010014

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