Başak, Ahmet Eren2017-10-042017-10-042017-092017-092017-10-02http://hdl.handle.net/11693/33775Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2017.Includes bibliographical references (leaves 46-50).We 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.x, 50 leaves : illustrations (some color), charts ; 30 cm.Englishinfo:eu-repo/semantics/openAccessEmotion contagionCrowd simulationParameter learningData-driven optimizationThesisB019008