research-article

Path patterns: analyzing and comparing real and simulated crowds

Authors:

Carol O'SullivanAuthors Info & Claims

I3D '16: Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

Pages 49 - 57

https://doi.org/10.1145/2856400.2856410

Published: 27 February 2016 Publication History

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.

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Cited By

Talukdar BZhang YWeiss T(2024)Learning Crowd Motion Dynamics with CrowdsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36513027:1(1-17)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3651302
Xiang WWang HZhang YYip MJin X(2023)Model‐based Crowd Behaviours in Human‐solution SpaceComputer Graphics Forum10.1111/cgf.1491942:6Online publication date: 6-Jul-2023
https://doi.org/10.1111/cgf.14919
Yao XWang SSun WWang HWang YJin X(2023)Crowd Simulation with Detailed Body Motion and InteractionAdvances in Computer Graphics10.1007/978-3-031-23473-6_18(227-238)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-031-23473-6_18
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Index Terms

Path patterns: analyzing and comparing real and simulated crowds
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Motion processing
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Topic modeling
    2. Machine learning approaches
      1. Learning in probabilistic graphical models
        Bayesian network models

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cover image ACM Conferences

I3D '16: Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

February 2016

200 pages

ISBN:9781450340434

DOI:10.1145/2856400

General Chairs:
Chris Wyman
NVIDIA Research
,
Cem Yuksel
The University of Utah

Copyright © 2016 ACM.

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Published: 27 February 2016

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I3D '16: Symposium on Interactive 3D Graphics and Games

February 27 - 28, 2016

Washington, Redmond

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Overall Acceptance Rate 148 of 485 submissions, 31%

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Cited By

Talukdar BZhang YWeiss T(2024)Learning Crowd Motion Dynamics with CrowdsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36513027:1(1-17)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3651302
Xiang WWang HZhang YYip MJin X(2023)Model‐based Crowd Behaviours in Human‐solution SpaceComputer Graphics Forum10.1111/cgf.1491942:6Online publication date: 6-Jul-2023
https://doi.org/10.1111/cgf.14919
Yao XWang SSun WWang HWang YJin X(2023)Crowd Simulation with Detailed Body Motion and InteractionAdvances in Computer Graphics10.1007/978-3-031-23473-6_18(227-238)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-031-23473-6_18
Zhong JLi DHuang ZLu CCai W(2022)Data-driven Crowd Modeling Techniques: A SurveyACM Transactions on Modeling and Computer Simulation10.1145/348129932:1(1-33)Online publication date: 7-Jan-2022
https://dl.acm.org/doi/10.1145/3481299
Yue JManocha DWang H(2022)Human Trajectory Prediction via Neural Social PhysicsComputer Vision – ECCV 202210.1007/978-3-031-19830-4_22(376-394)Online publication date: 22-Oct-2022
https://doi.org/10.1007/978-3-031-19830-4_22
Zhang BZhang RBisagno NConci NDe Natale FLiu H(2021)Where Are They Going? Predicting Human Behaviors in Crowded ScenesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/344935917:4(1-19)Online publication date: 12-Nov-2021
https://dl.acm.org/doi/10.1145/3449359
Sohre NGuy S(2020)SPNets: Human-like Navigation Behaviors with Uncertain GoalsProceedings of the 13th ACM SIGGRAPH Conference on Motion, Interaction and Games10.1145/3424636.3426911(1-11)Online publication date: 16-Oct-2020
https://dl.acm.org/doi/10.1145/3424636.3426911
He FXiang YZhao XWang H(2020)Informative scene decomposition for crowd analysis, comparison and simulation guidanceACM Transactions on Graphics10.1145/3386569.339240739:4(50:1-50:13)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.1145/3386569.3392407
van Toll WGrzeskowiak FGandía AAmirian JBerton FBruneau JDaniel BJovane APettré J(2020)Generalized Microscropic Crowd Simulation using Costs in Velocity SpaceSymposium on Interactive 3D Graphics and Games10.1145/3384382.3384532(1-9)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.1145/3384382.3384532
Mathew CBenes BAliaga D(2020)Interactive Inverse Spatio-Temporal Crowd Motion DesignSymposium on Interactive 3D Graphics and Games10.1145/3384382.3384528(1-9)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.1145/3384382.3384528
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