Parameter estimation and comparative evaluation of crowd simulations

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.
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Contributor : Julien Pettré <>
Submitted on : Monday, September 1, 2014 - 10:43:58 AM
Last modification on : Thursday, November 15, 2018 - 11:57:50 AM

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David Wolinski, Stephen J. Guy, Anne-Hélène Olivier, Ming Lin, Dinesh Manocha, et al.. Parameter estimation and comparative evaluation of crowd simulations. Computer Graphics Forum, Wiley, 2014, Eurographics 2014, 33 (2), pp.303--312. ⟨10.1111/cgf.12328⟩. ⟨hal-01059493⟩



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