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dc.contributor.advisorGüdükbay, Uğuren_US
dc.contributor.authorBaşak, Ahmet Erenen_US
dc.date.accessioned2017-10-04T12:45:01Z
dc.date.available2017-10-04T12:45:01Z
dc.date.copyright2017-09
dc.date.issued2017-10
dc.date.submitted2017-10-02
dc.identifier.urihttp://hdl.handle.net/11693/33775
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 46-50).en_US
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 in 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.en_US
dc.description.statementofresponsibilityby Ahmet Eren Başak.en_US
dc.format.extentx, 50 leaves : illustrations (some color), charts ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEmotion contagionen_US
dc.subjectCrowd simulationen_US
dc.subjectParameter learningen_US
dc.subjectData-driven optimizationen_US
dc.titleLearning from real-life experiences: a data-driven emotion contagion approach towards realistic virtual crowdsen_US
dc.title.alternativeGerçek olaylardan öğrenme: gerçekçi sanal kalabalıklar için veriye dayalı duygu bulaşıcılığıen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB019008
dc.embargo.release2020-10-02


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