Multiple Edge Computing Devices with Computer Vision for Social Distancing

Coronavirus disease, widely known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Once infected, a person can spread the virus through their nose or mouth in small particles when they cough, sneeze, speak, or breathe. According to the World Health Organization (WHO), one way to...

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Main Authors: Guico, Maria Leonora, Oppus, Carlos M, Monje, Jose Claro N, Kwong, John Chris T, Ngo, Gwendolyn, Belarmino, Mark Daniel, Mamaril, Cris Emmanuel Cirglen, Ngo, Genevieve C
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Published: Archīum Ateneo 2022
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Online Access:https://archium.ateneo.edu/ecce-faculty-pubs/133
https://doi.org/10.1109/TENCON55691.2022.9978047
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.ecce-faculty-pubs-11272023-01-26T07:11:14Z Multiple Edge Computing Devices with Computer Vision for Social Distancing Guico, Maria Leonora Oppus, Carlos M Monje, Jose Claro N Kwong, John Chris T Ngo, Gwendolyn Belarmino, Mark Daniel Mamaril, Cris Emmanuel Cirglen Ngo, Genevieve C Coronavirus disease, widely known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Once infected, a person can spread the virus through their nose or mouth in small particles when they cough, sneeze, speak, or breathe. According to the World Health Organization (WHO), one way to be protected from the risk of virus infection is to stay at least 1 meter apart from others while wearing a properly filtered mask. The study aims to design and develop a multiple edge computing system with computer vision capabilities to monitor the adherence of social distancing in multiple locations and in real time. An edge computing device uses a camera to process a stream of images. Graphical Processing Unit (GPU) was utilized for faster inference processing to detect people. The person's location will undergo transformation to get a 2D perspective. Then, a distance calculation algorithm will be imposed to each pair of persons detected to detect breach of social distancing protocol. For every breach detected, location coordinates will be sent to the host database for visualization and monitoring. The use of multiple edge computing devices for computer vision application was compared to the IP camera system in monitoring multiple locations. It is found that utilization of multiple edge computing devices has significant advantages in terms of power consumption, data acquisition, image processing and inference, and setup cost. 2022-01-01T08:00:00Z text https://archium.ateneo.edu/ecce-faculty-pubs/133 https://doi.org/10.1109/TENCON55691.2022.9978047 Electronics, Computer, and Communications Engineering Faculty Publications Archīum Ateneo computer vision edge computing perspective transformation social distance Computer Engineering Engineering Medicine and Health Sciences Physical Sciences and Mathematics
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic computer vision
edge computing
perspective transformation
social distance
Computer Engineering
Engineering
Medicine and Health Sciences
Physical Sciences and Mathematics
spellingShingle computer vision
edge computing
perspective transformation
social distance
Computer Engineering
Engineering
Medicine and Health Sciences
Physical Sciences and Mathematics
Guico, Maria Leonora
Oppus, Carlos M
Monje, Jose Claro N
Kwong, John Chris T
Ngo, Gwendolyn
Belarmino, Mark Daniel
Mamaril, Cris Emmanuel Cirglen
Ngo, Genevieve C
Multiple Edge Computing Devices with Computer Vision for Social Distancing
description Coronavirus disease, widely known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Once infected, a person can spread the virus through their nose or mouth in small particles when they cough, sneeze, speak, or breathe. According to the World Health Organization (WHO), one way to be protected from the risk of virus infection is to stay at least 1 meter apart from others while wearing a properly filtered mask. The study aims to design and develop a multiple edge computing system with computer vision capabilities to monitor the adherence of social distancing in multiple locations and in real time. An edge computing device uses a camera to process a stream of images. Graphical Processing Unit (GPU) was utilized for faster inference processing to detect people. The person's location will undergo transformation to get a 2D perspective. Then, a distance calculation algorithm will be imposed to each pair of persons detected to detect breach of social distancing protocol. For every breach detected, location coordinates will be sent to the host database for visualization and monitoring. The use of multiple edge computing devices for computer vision application was compared to the IP camera system in monitoring multiple locations. It is found that utilization of multiple edge computing devices has significant advantages in terms of power consumption, data acquisition, image processing and inference, and setup cost.
format text
author Guico, Maria Leonora
Oppus, Carlos M
Monje, Jose Claro N
Kwong, John Chris T
Ngo, Gwendolyn
Belarmino, Mark Daniel
Mamaril, Cris Emmanuel Cirglen
Ngo, Genevieve C
author_facet Guico, Maria Leonora
Oppus, Carlos M
Monje, Jose Claro N
Kwong, John Chris T
Ngo, Gwendolyn
Belarmino, Mark Daniel
Mamaril, Cris Emmanuel Cirglen
Ngo, Genevieve C
author_sort Guico, Maria Leonora
title Multiple Edge Computing Devices with Computer Vision for Social Distancing
title_short Multiple Edge Computing Devices with Computer Vision for Social Distancing
title_full Multiple Edge Computing Devices with Computer Vision for Social Distancing
title_fullStr Multiple Edge Computing Devices with Computer Vision for Social Distancing
title_full_unstemmed Multiple Edge Computing Devices with Computer Vision for Social Distancing
title_sort multiple edge computing devices with computer vision for social distancing
publisher Archīum Ateneo
publishDate 2022
url https://archium.ateneo.edu/ecce-faculty-pubs/133
https://doi.org/10.1109/TENCON55691.2022.9978047
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