Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/587433
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dc.contributor.authorWenxi Liuen_US
dc.contributor.authorRynson W. H. Lauen_US
dc.contributor.authorXiaogang Wangen_US
dc.contributor.authorDinesh Manochaen_US
dc.date.accessioned2020-05-20T08:37:28Z-
dc.date.available2020-05-20T08:37:28Z-
dc.date.issued2016en_US
dc.identifier.issn1520-9210en_US
dc.identifier.issn1941-0077en_US
dc.identifier.other10.1109/TMM.2016.2598091en_US
dc.identifier.urihttp://localhost/handle/Hannan/172248en_US
dc.identifier.urihttp://localhost/handle/Hannan/587433-
dc.description.abstractIn this paper, we present a novel method to recognize the types of crowd movement from crowd trajectories using agent-based motion models (AMMs). Our idea is to apply a number of AMMs, referred to as exemplar-AMMs, to describe the crowd movement. Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature. We then address our real-world crowd movement recognition problem as a multilabel classification problem. Our experiments show that the proposed feature outperforms the state-of-the-art methods in recognizing both simulated and real-world crowd movements from their trajectories. Finally, we have created a synthetic dataset, SynCrowd, which contains two-dimensional (2D) crowd trajectories in various scenarios, generated by various crowd simulators. This dataset can serve as a training set or benchmark for crowd analysis work.en_US
dc.publisherIEEEen_US
dc.relation.haspart7530845.pdfen_US
dc.subjectCrowd behavior modeling|pattern recognition|crowd simulation|video surveillanceen_US
dc.titleExemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectoriesen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Multimediaen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorWenxi Liuen_US
dc.contributor.authorRynson W. H. Lauen_US
dc.contributor.authorXiaogang Wangen_US
dc.contributor.authorDinesh Manochaen_US
dc.date.accessioned2020-05-20T08:37:28Z-
dc.date.available2020-05-20T08:37:28Z-
dc.date.issued2016en_US
dc.identifier.issn1520-9210en_US
dc.identifier.issn1941-0077en_US
dc.identifier.other10.1109/TMM.2016.2598091en_US
dc.identifier.urihttp://localhost/handle/Hannan/172248en_US
dc.identifier.urihttp://localhost/handle/Hannan/587433-
dc.description.abstractIn this paper, we present a novel method to recognize the types of crowd movement from crowd trajectories using agent-based motion models (AMMs). Our idea is to apply a number of AMMs, referred to as exemplar-AMMs, to describe the crowd movement. Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature. We then address our real-world crowd movement recognition problem as a multilabel classification problem. Our experiments show that the proposed feature outperforms the state-of-the-art methods in recognizing both simulated and real-world crowd movements from their trajectories. Finally, we have created a synthetic dataset, SynCrowd, which contains two-dimensional (2D) crowd trajectories in various scenarios, generated by various crowd simulators. This dataset can serve as a training set or benchmark for crowd analysis work.en_US
dc.publisherIEEEen_US
dc.relation.haspart7530845.pdfen_US
dc.subjectCrowd behavior modeling|pattern recognition|crowd simulation|video surveillanceen_US
dc.titleExemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectoriesen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Multimediaen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7530845.pdf717.18 kBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWenxi Liuen_US
dc.contributor.authorRynson W. H. Lauen_US
dc.contributor.authorXiaogang Wangen_US
dc.contributor.authorDinesh Manochaen_US
dc.date.accessioned2020-05-20T08:37:28Z-
dc.date.available2020-05-20T08:37:28Z-
dc.date.issued2016en_US
dc.identifier.issn1520-9210en_US
dc.identifier.issn1941-0077en_US
dc.identifier.other10.1109/TMM.2016.2598091en_US
dc.identifier.urihttp://localhost/handle/Hannan/172248en_US
dc.identifier.urihttp://localhost/handle/Hannan/587433-
dc.description.abstractIn this paper, we present a novel method to recognize the types of crowd movement from crowd trajectories using agent-based motion models (AMMs). Our idea is to apply a number of AMMs, referred to as exemplar-AMMs, to describe the crowd movement. Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature. We then address our real-world crowd movement recognition problem as a multilabel classification problem. Our experiments show that the proposed feature outperforms the state-of-the-art methods in recognizing both simulated and real-world crowd movements from their trajectories. Finally, we have created a synthetic dataset, SynCrowd, which contains two-dimensional (2D) crowd trajectories in various scenarios, generated by various crowd simulators. This dataset can serve as a training set or benchmark for crowd analysis work.en_US
dc.publisherIEEEen_US
dc.relation.haspart7530845.pdfen_US
dc.subjectCrowd behavior modeling|pattern recognition|crowd simulation|video surveillanceen_US
dc.titleExemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectoriesen_US
dc.typeArticleen_US
dc.journal.volume18en_US
dc.journal.issue12en_US
dc.journal.titleIEEE Transactions on Multimediaen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7530845.pdf717.18 kBAdobe PDFThumbnail
Preview File