Group Behavior from Video: A Data-Driven Approach to Crowd Simulation

Abstract

Crowd simulation techniques have frequently been used to animate a large group of virtual humans in computer graphics applications. We present a data-driven method of simulating a crowd of virtual humans that exhibit behaviors imitating real human crowds. To do so, we record the motion of a human crowd from an aerial view using a camcorder, extract the two-dimensional moving trajectories of each individual in the crowd, and then learn an agent model from observed trajectories. The agent model decides each agent s actions based on features of the environment and the motion of nearby agents in the crowd. Once the agent model is learned, we can simulate a virtual crowd that behaves similarly to the real crowd in the video. The versatility and flexibility of our approach is demonstrated through examples in which various characteristics of group behaviors are captured and reproduced in simulated crowds.