Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information

FláviaC. Delicato¹  PauloF. Pires¹  ThiagoP. Silva²  Aluizio Rocha Neto²  Thais Batista²  Frederico Lopes³ 

[1] Computer Science Department, Fluminense Federal University (UFF), Niteroi 24220-900, Brazil;Department of Informatics and Applied Mathematics, Federal University of RN (UFRN), Natal 59078-970, Brazil;Digital Metropolis Institute, Federal University of RN (UFRN), Natal 58078-970, Brazil;

关键词: video analytics;    edge computing;    machine learning;    edge intelligence;    messaging pattern;   

DOI  :  10.3390/info12010014

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown