People Counting in the Times of Covid-19
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
; 13821 LNCS:196-208, 2023.
Article
in English
| Scopus | ID: covidwho-2299412
ABSTRACT
Estimating the number of people within a public building with multiple entrances is an interesting problem, especially when limitations on building occupancy hold as during the Covid-19 pandemic. In this article, we illustrate the design, prototyping and assessment of an open-source distributed Cloud-IoT service that performs such a task and detects crowd formation via EdgeAI, also accounting for privacy and security concerns. The service is deployed and thoroughly assessed over a low-cost Fog infrastructure, showing an average accuracy of 94%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Year:
2023
Document Type:
Article
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