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• W2893684482 abstract "We present a data-driven algorithm to model and predict the socio-emotional impact of groups on observers. Psychological research finds that highly entitative i.e. cohesive and uniform groups induce threat and unease in observers. Our algorithm models realistic trajectory-level behaviors to classify and map the motion-based entitativity of crowds. This mapping is based on a statistical scheme that dynamically learns pedestrian behavior and computes the resultant entitativity induced emotion through group motion characteristics. We also present a novel interactive multi-agent simulation algorithm to model entitative groups and conduct a VR user study to validate the socio-emotional predictive power of our algorithm. We further show that model-generated high-entitativity groups do induce more negative emotions than low-entitative groups." @default.
• W2893684482 created "2018-10-05" @default.
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• W2893684482 date "2018-11-28" @default.
• W2893684482 modified "2023-10-17" @default.
• W2893684482 title "Data-driven modeling of group entitativity in virtual environments" @default.
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• W2893684482 doi "https://doi.org/10.1145/3281505.3281524" @default.
• W2893684482 hasPublicationYear "2018" @default.
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