University of North Carolina, Chapel Hill


Real World Data Real World Data

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.

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Best, A., Narang, S., Curtis, S., & Manocha, D. (2014). DenseSense: Interactive Crowd Simulation using Density-Dependent Filters. In Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation, pp. 97-102. Eurographics Association, 2014.

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Narang, S., Best, A., Curtis, S., & Manocha, D. (2015). Generating Pedestrian Trajectories Consistent with the Fundamental Diagram based on Physiological and Psychological Factors. PLOS ONE 10(4): e0117856 doi: 10.1371/journal.pone.0117856

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GAMMA Research Group
UNC Dept. of Computer Science