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dc.contributor.authorBelhadi, Asma
dc.contributor.authorDjenouri, Youcef
dc.contributor.authorSrivastava, Gautam
dc.contributor.authorDjenouri, Djamel
dc.contributor.authorLin, Jerry Chun-Wei
dc.contributor.authorFortino, Giancarlo
dc.date.accessioned2021-10-12T11:53:27Z
dc.date.available2021-10-12T11:53:27Z
dc.date.created2021-04-12T14:05:56Z
dc.date.issued2021
dc.identifier.citationBelhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Lin, J. C.-W., & Fortino, G. (2021). Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection. Information Fusion, 65, 13-20.en_US
dc.identifier.issn1566-2535
dc.identifier.urihttps://hdl.handle.net/11250/2789313
dc.description.abstractThis paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjecthuman behaviorsen_US
dc.subjectdeep learningen_US
dc.subjectdata miningen_US
dc.subjectanalysisen_US
dc.subjectsmart citiesen_US
dc.titleDeep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detectionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2020 The Authorsen_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.source.pagenumber13-20en_US
dc.source.volume65en_US
dc.source.journalInformation Fusionen_US
dc.identifier.doi10.1016/j.inffus.2020.08.003
dc.identifier.cristin1903553
dc.relation.projectResearch Council of Canada: RGPIN-2020-05363en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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