An emotion evolution based model for collective behavior simulation

Hao Jiang, Zhigang Deng, Mingliang Xu, Xiangjun He, Tianlu Mao, Zhaoqi Wang

Abstract: Current crowd simulation progresses still fall short of simulating many real-world collective behaviors. Arguably, one of the main reasons is that some essential qualities of human beings such as emotion have not been effectively modeled and incorporated into crowd simulation algorithms. In this paper, we propose a novel computational model for emotion evolution and demonstrate its applications for crowd simulation. Specifically, our approach is designed to tackle three major issues in the emotion evolution process: (i) how to perceive and evaluate emotion when individuals face emergency or external events, (ii) how to evolve the emotion during induction, and (iii) how specific actions of individuals in a crowd are impacted by emotion. Through many experiments, we demonstrate that our method can effectively simulate emergent dynamic collective patterns observed in real-world crowd footages.