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 language||English (US)|
|Title of host publication||SIGGRAPH Asia 2018 Technical Papers, SIGGRAPH Asia 2018|
|Publisher||Association for Computing Machinery, Inc|
|State||Published - Dec 4 2018|
|Event||SIGGRAPH Asia 2018 Technical Papers - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH Asia 2018 - Tokyo, Japan|
Duration: Dec 4 2018 → Dec 7 2018
|Name||SIGGRAPH Asia 2018 Technical Papers, SIGGRAPH Asia 2018|
|Conference||SIGGRAPH Asia 2018 Technical Papers - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH Asia 2018|
|Period||12/4/18 → 12/7/18|
Bibliographical noteFunding 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.
© 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
- Crowd simulation
- Manifold learning