Data-Driven crowd simulation

In this work we propose a framework for data-driven crowd simulation starting from a small set of trajectories. To model the macroscopic behavior, our method extracts pedestrian trajectories from real videos, clusters all trajectories of pedestrians who intend to reach the same goal, and computes the velocity field associated with each exit region in the scene to guide virtual agents toward their destinations, namely, the goal-dependent path selection. While at the microscopic level, the simulation is performed using on the one hand the Social Force Model to handle the collision-avoidance among agents and on the other hand the computed velocity fields to model the macroscopic behavior. The experimental results demonstrate that the velocity field can be exploited to effectively reproduce crowd behaviors.