Collective Crowd Formation Transform with Mutual Information–Based Runtime Feedback. (18th August 2014) - Xu, Mingliang; Wu, Yunpeng; Ye, Yangdong; Farkas, Illes; Jiang, Hao; Deng, Zhigang
<x xml:space="preserve">Abstract</x> This paper introduces a new crowd formation transform approach to achieve visually pleasing group formation transition and control. Its core idea is to transform crowd formation shapes with a least‐effort pair assignment using the Kuhn–Munkres algorithm, discover clusters of agent subgroups using affinity propagation and Delaunay triangulation algorithms, and apply subgroup‐based SFM (social force model) to the agent subgroups to achieve alignment, cohesion and collision avoidance. <boxed-text content-type="graphic" position="anchor" orientation="portrait"> </boxed-text>