Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute
Over the past decades, crowd management has attracted a great deal of attention in the area of video surveillance. Among various tasks of video surveillance analysis, crowd motion analysis is the basis of numerous subsequent applications of surveillance video. In this paper, a novel social force gra...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/492051 |
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| _version_ | 1832555544334827520 |
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| author | Shaonian Huang Dongjun Huang Mansoor Ahmed Khuhro |
| author_facet | Shaonian Huang Dongjun Huang Mansoor Ahmed Khuhro |
| author_sort | Shaonian Huang |
| collection | DOAJ |
| description | Over the past decades, crowd management has attracted a great deal of attention in the area of video surveillance. Among various tasks of video surveillance analysis, crowd motion analysis is the basis of numerous subsequent applications of surveillance video. In this paper, a novel social force graph with streak flow attribute is proposed to capture the global spatiotemporal changes and the local motion of crowd video. Crowd motion analysis is hereby implemented based on the characteristics of social force graph. First, the streak flow of crowd sequence is extracted to represent the global crowd motion; after that, spatiotemporal analogous patches are obtained based on the crowd visual features. A weighted social force graph is then constructed based on multiple social properties of crowd video. The graph is segmented into particle groups to represent the similar motion patterns of crowd video. A codebook is then constructed by clustering all local particle groups, and consequently crowd abnormal behaviors are detected by using the Latent Dirichlet Allocation model. Extensive experiments on challenging datasets show that the proposed method achieves preferable results in the application of crowd motion segmentation and abnormal behavior detection. |
| format | Article |
| id | doaj-art-825d64e3b4244586b11eb38b896733fd |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-825d64e3b4244586b11eb38b896733fd2025-02-03T05:47:53ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/492051492051Crowd Motion Analysis Based on Social Force Graph with Streak Flow AttributeShaonian Huang0Dongjun Huang1Mansoor Ahmed Khuhro2School of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaOver the past decades, crowd management has attracted a great deal of attention in the area of video surveillance. Among various tasks of video surveillance analysis, crowd motion analysis is the basis of numerous subsequent applications of surveillance video. In this paper, a novel social force graph with streak flow attribute is proposed to capture the global spatiotemporal changes and the local motion of crowd video. Crowd motion analysis is hereby implemented based on the characteristics of social force graph. First, the streak flow of crowd sequence is extracted to represent the global crowd motion; after that, spatiotemporal analogous patches are obtained based on the crowd visual features. A weighted social force graph is then constructed based on multiple social properties of crowd video. The graph is segmented into particle groups to represent the similar motion patterns of crowd video. A codebook is then constructed by clustering all local particle groups, and consequently crowd abnormal behaviors are detected by using the Latent Dirichlet Allocation model. Extensive experiments on challenging datasets show that the proposed method achieves preferable results in the application of crowd motion segmentation and abnormal behavior detection.http://dx.doi.org/10.1155/2015/492051 |
| spellingShingle | Shaonian Huang Dongjun Huang Mansoor Ahmed Khuhro Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute Journal of Electrical and Computer Engineering |
| title | Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute |
| title_full | Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute |
| title_fullStr | Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute |
| title_full_unstemmed | Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute |
| title_short | Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute |
| title_sort | crowd motion analysis based on social force graph with streak flow attribute |
| url | http://dx.doi.org/10.1155/2015/492051 |
| work_keys_str_mv | AT shaonianhuang crowdmotionanalysisbasedonsocialforcegraphwithstreakflowattribute AT dongjunhuang crowdmotionanalysisbasedonsocialforcegraphwithstreakflowattribute AT mansoorahmedkhuhro crowdmotionanalysisbasedonsocialforcegraphwithstreakflowattribute |