Parameter estimation and comparative evaluation of crowd simulations

D. Wolinski, S. J. Guy, A. H. Olivier, M. Lin, D. Manocha, J. Pettré

Research output: Contribution to journalArticlepeer-review

80 Scopus citations

Abstract

We present a novel framework to evaluate multi-agent crowd simulation algorithms based on real-world observations of crowd movements. A key aspect of our approach is to enable fair comparisons by automatically estimating the parameters that enable the simulation algorithms to best fit the given data. We formulate parameter estimation as an optimization problem, and propose a general framework to solve the combinatorial optimization problem for all parameterized crowd simulation algorithms. Our framework supports a variety of metrics to compare reference data and simulation outputs. The reference data may correspond to recorded trajectories, macroscopic parameters, or artist-driven sketches. We demonstrate the benefits of our framework for example-based simulation, modeling of cultural variations, artist-driven crowd animation, and relative comparison of some widely-used multi-agent simulation algorithms.

Original languageEnglish (US)
Pages (from-to)303-312
Number of pages10
JournalComputer Graphics Forum
Volume33
Issue number2
DOIs
StatePublished - May 2014

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