SteerFit: Automated parameter fitting for steering algorithms

Glen Berseth, Mubbasir Kapadia, Brandon Haworth, Petros Faloutsos

Research output: Contribution to conferencePaperpeer-review

39 Scopus citations

Abstract

In the context of crowd simulation, there is a diverse set of algorithms that model steering. The performance of steering approaches, both in terms of quality of results and computational efficiency, depends on internal parameters that are manually tuned to satisfy application-specific requirements. This paper investigates the effect that these parameters have on an algorithm's performance. Using three representative steering algorithms and a set of established performance criteria, we perform a number of large scale optimization experiments that optimize an algorithm's parameters for a range of objectives. For example, our method automatically finds optimal parameters to minimize turbulence at bottlenecks, reduce building evacuation times, produce emergent patterns, and increase the computational efficiency of an algorithm. We also propose using the Pareto Optimal front as an efficient way of modelling optimal relationships between multiple objectives, and demonstrate its effectiveness by estimating optimal parameters for interactively defined combinations of the associated objectives. The proposed methodologies are general and can be applied to any steering algorithm using any set of performance criteria.

Original languageEnglish (US)
Pages113-122
Number of pages10
StatePublished - Jul 21 2014
Externally publishedYes
Event2014 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2014 - Copenhagen, Denmark
Duration: Jul 21 2014Jul 23 2014

Other

Other2014 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2014
Country/TerritoryDenmark
CityCopenhagen
Period7/21/147/23/14

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Software
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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