DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

Rezaei, M orcid.org/0000-0003-3892-421X and Azarmi, M (2020) DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic. Applied Sciences, 10 (21). 7514. ISSN 2076-3417

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)
Keywords: social distancing; COVID-19; human detection and tracking; distance estimation; deep convolutional neural networks; crowd monitoring; pedestrian detection; inverse perspective mapping
Dates:
  • Published: November 2020
  • Accepted: 20 October 2020
  • Published (online): 26 October 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 27 Nov 2020 12:51
Last Modified: 27 Nov 2020 12:51
Status: Published
Publisher: MDPI
Identification Number: https://doi.org/10.3390/app10217514

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