Wang, H orcid.org/0000-0002-2281-5679, Ondřej, J and O'Sullivan, C (2016) Path Patterns: Analyzing and Comparing Real and Simulated Crowds. In: Wyman, C, Yuksel, C and Spencer, SN, (eds.) Proceedings. I3D '16: 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 26-28 Feb 2016, Redmond, WA, USA. ACM , pp. 49-57. ISBN 978-1-4503-4043-4
Abstract
Crowd simulation has been an active and important area of research in the field of interactive 3D graphics for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose a new approach based on finding latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd's behaviour. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is then computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2016, The Authors. Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, https://doi.org/10.1145/2856400.2856410. |
Keywords: | Crowd Simulation, Crowd Comparison, Data-Driven, Clustering, Hierarchical Dirichlet Process, Stochastic Optimization |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Oct 2016 10:26 |
Last Modified: | 03 Nov 2017 04:54 |
Published Version: | https://doi.org/10.1145/2856400.2856410 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/2856400.2856410 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:106101 |