Learning from real-life experiences: a data-driven emotion contagion approach towards realistic virtual crowds
Başak, Ahmet Eren
Embargo Release Date2020-10-02
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/33775
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.