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Crowd Space: A Predictive Crowd Analysis Technique

Ioannis Karamouzas1, Nick Sohre2, Ran Hu2,3, Stephen J. Guy2
1Clemson University, 2University of Minnesota, 3Facebook
In ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2018)


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.

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Bibtex

@article{crowdspace2018,
 author = {Karamouzas, Ioannis and Sohre, Nick and Hu, Ran and Guy, Stephen J.},
 title = {Crowd Space: A Predictive Crowd Analysis Technique},
 journal = {ACM Transactions on Graphics},
 volume = {37},
 number = {6},
 year = {2018},
 doi = {10.1145/3272127.3275079}
}