Crowd Space: A predictive crowd analysis technique

Ioannis Karamouzas, Nick Sohre, Ran Hu, Stephen J Guy

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Over the last two decades there has been a proliferation of methods for simulating crowds of humans. As the number of different methods and their complexity increases, it becomes increasingly unrealistic to expect researchers and users to keep up with all the possible options and trade-offs. We therefore see the need for tools that can facilitate both domain experts and non-expert users of crowd simulation in making high-level decisions about the best simulation methods to use in different scenarios. In this paper, we leverage trajectory data from human crowds and machine learning techniques to learn a manifold which captures representative local navigation scenarios that humans encounter in real life. We show the applicability of this manifold in crowd research, including analyzing trends in simulation accuracy, and creating automated systems to assist in choosing an appropriate simulation method for a given scenario.

Original languageEnglish (US)
Article number186
JournalACM Transactions on Graphics
Volume37
Issue number6
DOIs
StatePublished - Nov 2018

Bibliographical note

Funding Information:
We thank Jan Ondrej for sharing his T-model code, and Rahul Narain for useful discussions. This work was supported in part by the National Science Foundation under grants IIS-1748541, CHS-1526693, and CNS-1544887.

Funding Information:
We thank Jan Ondřej for sharing his T-model code, and Rahul Narain for useful discussions. This work was supported in part by the National Science Foundation under grants IIS-1748541, CHS-1526693, and CNS-1544887.

Keywords

  • Crowd simulation
  • Entropy
  • Manifold learning
  • Validation

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