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dc.contributor.authorBaşak, A. E.en_US
dc.contributor.authorGüdükbay, Uğuren_US
dc.contributor.authorDurupınar, F.en_US
dc.date.accessioned2019-02-21T16:01:24Z
dc.date.available2019-02-21T16:01:24Z
dc.date.issued2018en_US
dc.identifier.issn0097-8493
dc.identifier.urihttp://hdl.handle.net/11693/49843
dc.description.abstractWe propose a data-driven approach for tuning, validating and optimizing crowd simulations by learning parameters from real-life videos. We discuss the common traits of incidents and their video footages suitable for the learning step. We then demonstrate the learning process in three real-life incidents: a bombing attack, a panic situation on the subway and a Black Friday rush. We reanimate the incidents using an existing emotion contagion and crowd simulation framework and optimize the parameters that characterize agent behavior with respect to the data extracted from the video footages of the incidents.
dc.language.isoEnglish
dc.source.titleComputers & Graphics: an international journal of systems & applications in computer graphicsen_US
dc.relation.isversionofhttps://doi.org/10.1016/j.cag.2018.02.004
dc.subjectCrowd simulationen_US
dc.subjectData-driven optimizationen_US
dc.subjectEmotion contagionen_US
dc.subjectParameter learningen_US
dc.titleUsing real life incidents for creating realistic virtual crowds with data-driven emotion contagionen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage70en_US
dc.citation.epage81en_US
dc.citation.volumeNumber72en_US
dc.identifier.doi10.1016/j.cag.2018.02.004
dc.publisherPergamon Press
dc.contributor.bilkentauthorGüdükbay, Uğur
dc.embargo.release2020-05-01en_US


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