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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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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. |
format | Online Article Text |
id | pubmed-9005306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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