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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Leveraging Edge Intelligence for Video Analytics in Smart City Applications

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Autor(es):
Rocha Neto, Aluizio [1, 2] ; Silva, Thiago P. [1] ; Batista, Thais [1] ; Delicato, Flavia C. [3] ; Pires, Paulo F. [3] ; Lopes, Frederico [2]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Fed Univ RN UFRN, Dept Informat & Appl Math, BR-59078970 Natal, RN - Brazil
[2] Fed Univ RN UFRN, Digital Metropolis Inst, BR-59078970 Natal, RN - Brazil
[3] Fluminense Fed Univ UFF, Comp Sci Dept, BR-24220900 Niteroi, RJ - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: INFORMATION; v. 12, n. 1 JAN 2021.
Citações Web of Science: 0
Resumo

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. (AU)

Processo FAPESP: 15/24144-7 - Tecnologias e soluções para habilitar o paradigma de nuvens de coisas
Beneficiário:José Neuman de Souza
Modalidade de apoio: Auxílio à Pesquisa - Temático