An embedded toolset for human activity monitoring in critical environments
© 2022 Published by Elsevier Ltd.
| Veröffentlicht in: | Expert systems with applications. - 1999. - 199(2022) vom: 01. Aug., Seite 117125 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Expert systems with applications |
| Schlagworte: | Journal Article Computer vision Counting Deep learning Embedded system Homography Machine learning Personal protective equipment |
| Zusammenfassung: | © 2022 Published by Elsevier Ltd. 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 |
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| Beschreibung: | Date Revised 21.12.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2022.117125 |