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A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning

Coronavirus born COVID-19 disease has spread its roots in the whole world. It is primarily spread by physical contact. As a preventive measure, proper crowd monitoring and management systems are required to be installed in public places to limit sudden outbreaks and impart improved healthcare. The n...

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Autores principales: Yadav, Sangeeta, Gulia, Preeti, Gill, Nasib Singh, Chatterjee, Jyotir Moy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005306/
https://www.ncbi.nlm.nih.gov/pubmed/35422976
http://dx.doi.org/10.1155/2022/2130172
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author Yadav, Sangeeta
Gulia, Preeti
Gill, Nasib Singh
Chatterjee, Jyotir Moy
author_facet Yadav, Sangeeta
Gulia, Preeti
Gill, Nasib Singh
Chatterjee, Jyotir Moy
author_sort Yadav, Sangeeta
collection PubMed
description Coronavirus born COVID-19 disease has spread its roots in the whole world. It is primarily spread by physical contact. As a preventive measure, proper crowd monitoring and management systems are required to be installed in public places to limit sudden outbreaks and impart improved healthcare. The number of new infections can be significantly reduced by adopting social distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for social distance classification is proposed in this research paper. In the proposed system, people are segregated from the background using the YOLO v4 object detection technique, and then the detected people are tracked by bounding boxes using the Deepsort technique. This system significantly helps in COVID-19 prevention by social distance detection and classification in public places using surveillance images and videos captured by the cameras installed in these places. The performance of this system has been assessed using mean average precision (mAP) and frames per second (FPS) metrics. It has also been evaluated by deploying it on Jetson Nano, a low-cost embedded system. The observed results show its suitability for real-time deployment in public places for COVID-19 prevention by social distance monitoring and classification.
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spelling pubmed-90053062022-04-13 A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning Yadav, Sangeeta Gulia, Preeti Gill, Nasib Singh Chatterjee, Jyotir Moy J Healthc Eng Research Article Coronavirus born COVID-19 disease has spread its roots in the whole world. It is primarily spread by physical contact. As a preventive measure, proper crowd monitoring and management systems are required to be installed in public places to limit sudden outbreaks and impart improved healthcare. The number of new infections can be significantly reduced by adopting social distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for social distance classification is proposed in this research paper. In the proposed system, people are segregated from the background using the YOLO v4 object detection technique, and then the detected people are tracked by bounding boxes using the Deepsort technique. This system significantly helps in COVID-19 prevention by social distance detection and classification in public places using surveillance images and videos captured by the cameras installed in these places. The performance of this system has been assessed using mean average precision (mAP) and frames per second (FPS) metrics. It has also been evaluated by deploying it on Jetson Nano, a low-cost embedded system. The observed results show its suitability for real-time deployment in public places for COVID-19 prevention by social distance monitoring and classification. Hindawi 2022-04-05 /pmc/articles/PMC9005306/ /pubmed/35422976 http://dx.doi.org/10.1155/2022/2130172 Text en Copyright © 2022 Sangeeta Yadav et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yadav, Sangeeta
Gulia, Preeti
Gill, Nasib Singh
Chatterjee, Jyotir Moy
A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning
title A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning
title_full A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning
title_fullStr A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning
title_full_unstemmed A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning
title_short A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning
title_sort real-time crowd monitoring and management system for social distance classification and healthcare using deep learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005306/
https://www.ncbi.nlm.nih.gov/pubmed/35422976
http://dx.doi.org/10.1155/2022/2130172
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