Using real life incidents for creating realistic virtual crowds with data-driven emotion contagion

• Published in: Computers & graphics 2018-05, Vol.72, p.70-81
• Main Authors: Basak, Ahmet Eren, Güdükbay, Ugur, Durupinar, Funda
• Format: Article
• Language: English
• Subjects: Computer simulation; Computers; Crowd simulation; Crowdsourcing; Data-driven optimization; Emotion contagion; Learning; Optimization; Parameter learning; Parameters; Scholars; Virtual reality
• ISSN: 0097-8493; 1873-7684
• DOI: 10.1016/j.cag.2018.02.004

•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 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 by optimizing the parameters that characterize agent behavior according to the data extracted from the video footages. [Display omitted] 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 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.