Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis
<p dir="ltr">In recent years, crowd behavior detection has posed significant challenges in the realm of public safety and security, even with the advancements in surveillance technologies. The ability to perform real-time surveillance and accurately identify crowd behavior by conside...
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2024
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| author | Marwa Qaraqe (10135172) |
| author2 | Yin David Yang (19160746) Elizabeth B Varghese (22155301) Emrah Basaran (19160743) Almiqdad Elzein (13141038) |
| author2_role | author author author author |
| author_facet | Marwa Qaraqe (10135172) Yin David Yang (19160746) Elizabeth B Varghese (22155301) Emrah Basaran (19160743) Almiqdad Elzein (13141038) |
| author_role | author |
| dc.creator.none.fl_str_mv | Marwa Qaraqe (10135172) Yin David Yang (19160746) Elizabeth B Varghese (22155301) Emrah Basaran (19160743) Almiqdad Elzein (13141038) |
| dc.date.none.fl_str_mv | 2024-08-26T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10489-024-05775-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Crowd_behavior_detection_leveraging_video_swin_transformer_for_crowd_size_and_violence_level_analysis/30023416 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Built environment and design Urban and regional planning Information and computing sciences Artificial intelligence Human-centred computing Machine learning Crowd behavior detection Swin transformer DeepStream Crowd size Violence Level |
| dc.title.none.fl_str_mv | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In recent years, crowd behavior detection has posed significant challenges in the realm of public safety and security, even with the advancements in surveillance technologies. The ability to perform real-time surveillance and accurately identify crowd behavior by considering factors such as crowd size and violence levels can avert potential crowd-related disasters and hazards to a considerable extent. However, most existing approaches are not viable to deal with the complexities of crowd dynamics and fail to distinguish different violence levels within crowds. Moreover, the prevailing approach to crowd behavior recognition, which solely relies on the analysis of closed-circuit television (CCTV) footage and overlooks the integration of online social media video content, leads to a primarily reactive methodology. This paper proposes a crowd behavior detection framework based on the swin transformer architecture, which leverages crowd counting maps and optical flow maps to detect crowd behavior across various sizes and violence levels. To support this framework, we created a dataset comprising videos capable of recognizing crowd behaviors based on size and violence levels sourced from CCTV camera footage and online videos. Experimental analysis conducted on benchmark datasets and our proposed dataset substantiates the superiority of our proposed approach over existing state-of-the-art methods, showcasing its ability to effectively distinguish crowd behaviors concerning size and violence level. Our method’s validation through Nvidia’s DeepStream Software Development Kit (SDK) highlights its competitive performance and potential for real-time intelligent surveillance applications.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10489-024-05775-6" target="_blank">https://dx.doi.org/10.1007/s10489-024-05775-6</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7534b2d5bb6fbe82d769857b622fa4a8 |
| identifier_str_mv | 10.1007/s10489-024-05775-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30023416 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysisMarwa Qaraqe (10135172)Yin David Yang (19160746)Elizabeth B Varghese (22155301)Emrah Basaran (19160743)Almiqdad Elzein (13141038)Built environment and designUrban and regional planningInformation and computing sciencesArtificial intelligenceHuman-centred computingMachine learningCrowd behavior detectionSwin transformerDeepStreamCrowd sizeViolence Level<p dir="ltr">In recent years, crowd behavior detection has posed significant challenges in the realm of public safety and security, even with the advancements in surveillance technologies. The ability to perform real-time surveillance and accurately identify crowd behavior by considering factors such as crowd size and violence levels can avert potential crowd-related disasters and hazards to a considerable extent. However, most existing approaches are not viable to deal with the complexities of crowd dynamics and fail to distinguish different violence levels within crowds. Moreover, the prevailing approach to crowd behavior recognition, which solely relies on the analysis of closed-circuit television (CCTV) footage and overlooks the integration of online social media video content, leads to a primarily reactive methodology. This paper proposes a crowd behavior detection framework based on the swin transformer architecture, which leverages crowd counting maps and optical flow maps to detect crowd behavior across various sizes and violence levels. To support this framework, we created a dataset comprising videos capable of recognizing crowd behaviors based on size and violence levels sourced from CCTV camera footage and online videos. Experimental analysis conducted on benchmark datasets and our proposed dataset substantiates the superiority of our proposed approach over existing state-of-the-art methods, showcasing its ability to effectively distinguish crowd behaviors concerning size and violence level. Our method’s validation through Nvidia’s DeepStream Software Development Kit (SDK) highlights its competitive performance and potential for real-time intelligent surveillance applications.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10489-024-05775-6" target="_blank">https://dx.doi.org/10.1007/s10489-024-05775-6</a></p>2024-08-26T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10489-024-05775-6https://figshare.com/articles/journal_contribution/Crowd_behavior_detection_leveraging_video_swin_transformer_for_crowd_size_and_violence_level_analysis/30023416CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300234162024-08-26T09:00:00Z |
| spellingShingle | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis Marwa Qaraqe (10135172) Built environment and design Urban and regional planning Information and computing sciences Artificial intelligence Human-centred computing Machine learning Crowd behavior detection Swin transformer DeepStream Crowd size Violence Level |
| status_str | publishedVersion |
| title | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis |
| title_full | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis |
| title_fullStr | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis |
| title_full_unstemmed | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis |
| title_short | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis |
| title_sort | Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis |
| topic | Built environment and design Urban and regional planning Information and computing sciences Artificial intelligence Human-centred computing Machine learning Crowd behavior detection Swin transformer DeepStream Crowd size Violence Level |