DenseSense: Interactive Crowd Simulation using Density-Dependent Filters

Abstract

We present a novel algorithm to model density-dependent behaviors in crowd simulation. Our approach aims to generate pedestrian trajectories that correspond to the speed/density relationships that are typically expressed using the Fundamental Diagram. The algorithm's formulation can be easily combined with well-known multi-agent simulation techniques that use social forces or reciprocal velocity obstacles for local navigation. Our approach results in better utilization of free space by the pedestrians and has a small computational overhead. We are able to generate human-like dense crowd behaviors in large indoor and outdoor environments; we validate our results by comparing them with captured crowd trajectories.