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An embedded toolset for human activity monitoring in critical environments

Academic Article
Publication Date:
2022
abstract:
In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people's behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions.
Iris type:
01.01 Articolo in rivista
Keywords:
Deep learning; Computer vision; Machine learning; Personal protective equipment; Counting; Homography; Embedded system
List of contributors:
Ciampi, Luca; Amato, Giuseppe; Gennaro, Claudio; DI BENEDETTO, Marco; Falchi, Fabrizio; Carrara, Fabio
Authors of the University:
AMATO GIUSEPPE
CARRARA FABIO
CIAMPI LUCA
DI BENEDETTO MARCO
FALCHI FABRIZIO
GENNARO CLAUDIO
Handle:
https://iris.cnr.it/handle/20.500.14243/446655
Published in:
EXPERT SYSTEMS WITH APPLICATIONS
Journal
  • Overview

Overview

URL

https://doi.org/10.1016/j.eswa.2022.117125
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