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Anomaly detection with vision-based deep learning for epidemic prevention and control

Hooman Samani, Chan-Yun Yang, Chunxu Li Orcid Logo, Chia-Ling Chung, Shaoxiang Li

Journal of Computational Design and Engineering, Volume: 9, Issue: 1, Pages: 187 - 200

Swansea University Author: Chunxu Li Orcid Logo

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DOI (Published version): 10.1093/jcde/qwab075

Abstract

During the COVID-19 pandemic, people were advised to keep a social distance from others. People’s behaviors will also be noticed, such as lying down because of illness, regarded as abnormal conditions. This paper proposes a visual anomaly analysis system based on deep learning to identify individual...

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Published in: Journal of Computational Design and Engineering
ISSN: 2288-5048
Published: Oxford University Press (OUP) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa66001
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first_indexed 2024-04-10T08:25:38Z
last_indexed 2024-04-10T08:25:38Z
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spelling v2 66001 2024-04-09 Anomaly detection with vision-based deep learning for epidemic prevention and control e6ed70d02c25b05ab52340312559d684 0000-0001-7851-0260 Chunxu Li Chunxu Li true false 2024-04-09 ACEM During the COVID-19 pandemic, people were advised to keep a social distance from others. People’s behaviors will also be noticed, such as lying down because of illness, regarded as abnormal conditions. This paper proposes a visual anomaly analysis system based on deep learning to identify individuals with various anomaly types. In the study, two types of anomaly detections are concerned. The first is monitoring the anomaly in the case of falling in an open public area. The second is measuring the social distance of people in the area to warn the individuals under a short distance. By implementing a deep model named You Only Look Once, the related anomaly can be identified accurately in a wide range of open spaces. Experimental results show that the detection accuracy of the proposed method is 91%. In the social distance, the actual social distance is calculated by calculating the plane distance to ensure that everyone can meet the specification. Integrating the two functions and implementing the environmental monitoring system will make it easier to monitor and manage the disease-related abnormalities on the site. Journal Article Journal of Computational Design and Engineering 9 1 187 200 Oxford University Press (OUP) 2288-5048 robotics for pandemics, anomaly detection, social distance, deep learning, computer vision, epidemic prevention and control 2 2 2022 2022-02-02 10.1093/jcde/qwab075 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Another institution paid the OA fee 2024-05-22T16:08:20.8169990 2024-04-09T20:06:18.4726590 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Hooman Samani 1 Chan-Yun Yang 2 Chunxu Li 0000-0001-7851-0260 3 Chia-Ling Chung 4 Shaoxiang Li 5 66001__30443__3e7eb2db75a544c3aea94c958ada8932.pdf 66001.VoR.pdf 2024-05-22T16:06:23.7099041 Output 6045560 application/pdf Version of Record true Copyright: The Author(s) 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution License. true eng https://creativecommons.org/licenses/by/4.0/
title Anomaly detection with vision-based deep learning for epidemic prevention and control
spellingShingle Anomaly detection with vision-based deep learning for epidemic prevention and control
Chunxu Li
title_short Anomaly detection with vision-based deep learning for epidemic prevention and control
title_full Anomaly detection with vision-based deep learning for epidemic prevention and control
title_fullStr Anomaly detection with vision-based deep learning for epidemic prevention and control
title_full_unstemmed Anomaly detection with vision-based deep learning for epidemic prevention and control
title_sort Anomaly detection with vision-based deep learning for epidemic prevention and control
author_id_str_mv e6ed70d02c25b05ab52340312559d684
author_id_fullname_str_mv e6ed70d02c25b05ab52340312559d684_***_Chunxu Li
author Chunxu Li
author2 Hooman Samani
Chan-Yun Yang
Chunxu Li
Chia-Ling Chung
Shaoxiang Li
format Journal article
container_title Journal of Computational Design and Engineering
container_volume 9
container_issue 1
container_start_page 187
publishDate 2022
institution Swansea University
issn 2288-5048
doi_str_mv 10.1093/jcde/qwab075
publisher Oxford University Press (OUP)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description During the COVID-19 pandemic, people were advised to keep a social distance from others. People’s behaviors will also be noticed, such as lying down because of illness, regarded as abnormal conditions. This paper proposes a visual anomaly analysis system based on deep learning to identify individuals with various anomaly types. In the study, two types of anomaly detections are concerned. The first is monitoring the anomaly in the case of falling in an open public area. The second is measuring the social distance of people in the area to warn the individuals under a short distance. By implementing a deep model named You Only Look Once, the related anomaly can be identified accurately in a wide range of open spaces. Experimental results show that the detection accuracy of the proposed method is 91%. In the social distance, the actual social distance is calculated by calculating the plane distance to ensure that everyone can meet the specification. Integrating the two functions and implementing the environmental monitoring system will make it easier to monitor and manage the disease-related abnormalities on the site.
published_date 2022-02-02T16:08:19Z
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score 11.037603

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