Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey
<p>Since the start of the COVID-19 pandemic, social distancing (SD) has played an essential role in controlling and slowing down the spread of the virus in smart cities. To ensure the respect of SD in public areas, visual SD monitoring (VSDM) provides promising opportunities by (i) controlling...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513536534773760 |
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| author | Yassine Himeur (14158821) |
| author2 | Somaya Al-Maadeed (5178131) Noor Almaadeed (14150898) Khalid Abualsaud (16888701) Amr Mohamed (3508121) Tamer Khattab (16870086) Omar Elharrouss (14150784) |
| author2_role | author author author author author author |
| author_facet | Yassine Himeur (14158821) Somaya Al-Maadeed (5178131) Noor Almaadeed (14150898) Khalid Abualsaud (16888701) Amr Mohamed (3508121) Tamer Khattab (16870086) Omar Elharrouss (14150784) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yassine Himeur (14158821) Somaya Al-Maadeed (5178131) Noor Almaadeed (14150898) Khalid Abualsaud (16888701) Amr Mohamed (3508121) Tamer Khattab (16870086) Omar Elharrouss (14150784) |
| dc.date.none.fl_str_mv | 2022-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.scs.2022.104064 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_visual_social_distancing_monitoring_to_combat_COVID-19_A_comprehensive_survey/24720255 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Computer vision and multimedia computation Machine learning Visual social distancing monitoring Pedestrian detection Euclidean distance Bird’s eye view Convolutional neural networks Transfer learning |
| dc.title.none.fl_str_mv | Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Since the start of the COVID-19 pandemic, social distancing (SD) has played an essential role in controlling and slowing down the spread of the virus in smart cities. To ensure the respect of SD in public areas, visual SD monitoring (VSDM) provides promising opportunities by (i) controlling and analyzing the physical distance between pedestrians in real-time, (ii) detecting SD violations among the crowds, and (iii) tracking and reporting individuals violating SD norms. To the authors’ best knowledge, this paper proposes the first comprehensive survey of VSDM frameworks and identifies their challenges and future perspectives. Typically, we review existing contributions by presenting the background of VSDM, describing evaluation metrics, and discussing SD datasets. Then, VSDM techniques are carefully reviewed after dividing them into two main categories: hand-crafted feature-based and deep-learning-based methods. A significant focus is paid to convolutional neural networks (CNN)-based methodologies as most of the frameworks have used either one-stage, two-stage, or multi-stage CNN models. A comparative study is also conducted to identify their pros and cons. Thereafter, a critical analysis is performed to highlight the issues and impediments that hold back the expansion of VSDM systems. Finally, future directions attracting significant research and development are derived.</p><h2>Other Information</h2> <p> Published in: Sustainable Cities and Society<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.scs.2022.104064" target="_blank">https://dx.doi.org/10.1016/j.scs.2022.104064</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_30796f9ada90b4180c0810c0c747751e |
| identifier_str_mv | 10.1016/j.scs.2022.104064 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24720255 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep visual social distancing monitoring to combat COVID-19: A comprehensive surveyYassine Himeur (14158821)Somaya Al-Maadeed (5178131)Noor Almaadeed (14150898)Khalid Abualsaud (16888701)Amr Mohamed (3508121)Tamer Khattab (16870086)Omar Elharrouss (14150784)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesComputer vision and multimedia computationMachine learningVisual social distancing monitoringPedestrian detectionEuclidean distanceBird’s eye viewConvolutional neural networksTransfer learning<p>Since the start of the COVID-19 pandemic, social distancing (SD) has played an essential role in controlling and slowing down the spread of the virus in smart cities. To ensure the respect of SD in public areas, visual SD monitoring (VSDM) provides promising opportunities by (i) controlling and analyzing the physical distance between pedestrians in real-time, (ii) detecting SD violations among the crowds, and (iii) tracking and reporting individuals violating SD norms. To the authors’ best knowledge, this paper proposes the first comprehensive survey of VSDM frameworks and identifies their challenges and future perspectives. Typically, we review existing contributions by presenting the background of VSDM, describing evaluation metrics, and discussing SD datasets. Then, VSDM techniques are carefully reviewed after dividing them into two main categories: hand-crafted feature-based and deep-learning-based methods. A significant focus is paid to convolutional neural networks (CNN)-based methodologies as most of the frameworks have used either one-stage, two-stage, or multi-stage CNN models. A comparative study is also conducted to identify their pros and cons. Thereafter, a critical analysis is performed to highlight the issues and impediments that hold back the expansion of VSDM systems. Finally, future directions attracting significant research and development are derived.</p><h2>Other Information</h2> <p> Published in: Sustainable Cities and Society<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.scs.2022.104064" target="_blank">https://dx.doi.org/10.1016/j.scs.2022.104064</a></p>2022-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.scs.2022.104064https://figshare.com/articles/journal_contribution/Deep_visual_social_distancing_monitoring_to_combat_COVID-19_A_comprehensive_survey/24720255CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247202552022-10-01T00:00:00Z |
| spellingShingle | Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey Yassine Himeur (14158821) Engineering Electronics, sensors and digital hardware Information and computing sciences Computer vision and multimedia computation Machine learning Visual social distancing monitoring Pedestrian detection Euclidean distance Bird’s eye view Convolutional neural networks Transfer learning |
| status_str | publishedVersion |
| title | Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey |
| title_full | Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey |
| title_fullStr | Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey |
| title_full_unstemmed | Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey |
| title_short | Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey |
| title_sort | Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Computer vision and multimedia computation Machine learning Visual social distancing monitoring Pedestrian detection Euclidean distance Bird’s eye view Convolutional neural networks Transfer learning |