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Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Authors:

Carol O'SullivanAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 23, Issue 5

Pages 1454 - 1464

https://doi.org/10.1109/TVCG.2016.2642963

Published: 01 May 2017 Publication History

Abstract

We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area 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 the first approach based on finding semantic information represented by 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 behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process ( SV-DHDP) model. The fidelity of the patterns is 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. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise.

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Cited By

Charalambous PPettre JVassiliades VChrysanthou YPelechano N(2023)GREIL-Crowds: Crowd Simulation with Deep Reinforcement Learning and ExamplesACM Transactions on Graphics10.1145/359245942:4(1-15)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592459
Wang XWu ZHuang WWei YHuang ZXu MChen W(2023)VIS+AI: integrating visualization with artificial intelligence for efficient data analysisFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-2691-y17:6Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1007/s11704-023-2691-y
Panayiotou AKyriakou TLemonari MChrysanthou YCharalambous P(2022)CCP: Configurable Crowd ProfilesACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530712(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3528233.3530712
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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 23, Issue 5

May 2017

147 pages

ISSN:1077-2626

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Copyright © 2017.

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IEEE Educational Activities Department

United States

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Published: 01 May 2017

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Cited By

Charalambous PPettre JVassiliades VChrysanthou YPelechano N(2023)GREIL-Crowds: Crowd Simulation with Deep Reinforcement Learning and ExamplesACM Transactions on Graphics10.1145/359245942:4(1-15)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592459
Wang XWu ZHuang WWei YHuang ZXu MChen W(2023)VIS+AI: integrating visualization with artificial intelligence for efficient data analysisFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-2691-y17:6Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1007/s11704-023-2691-y
Panayiotou AKyriakou TLemonari MChrysanthou YCharalambous P(2022)CCP: Configurable Crowd ProfilesACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530712(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3528233.3530712
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
Yao XWang SSun WWang HWang YJin X(2022)Crowd Simulation with Detailed Body Motion and InteractionAdvances in Computer Graphics10.1007/978-3-031-23473-6_18(227-238)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-23473-6_18
Hesham OWainer G(2021)Explicit Modeling of Personal Space for Improved Local Dynamics in Simulated CrowdsACM Transactions on Modeling and Computer Simulation10.1145/346220231:4(1-29)Online publication date: 23-Jul-2021
https://dl.acm.org/doi/10.1145/3462202
Xu MLi CLv PChen WDeng ZZhou BManocha D(2021)Emotion-Based Crowd Simulation Model Based on Physical Strength Consumption for Emergency ScenariosIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.300060722:11(6977-6991)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1109/TITS.2020.3000607
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
Agethen PGaisbauer FManns MLink MRukzio ECharalambous PChrysanthou YJones BLee J(2018)Towards realistic walk path simulation of single subjectsProceedings of the 11th ACM SIGGRAPH Conference on Motion, Interaction and Games10.1145/3274247.3274504(1-10)Online publication date: 8-Nov-2018
https://dl.acm.org/doi/10.1145/3274247.3274504
Karamouzas ISohre NHu RGuy S(2018)Crowd spaceACM Transactions on Graphics10.1145/3272127.327507937:6(1-14)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3272127.3275079
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