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A synthetic-vision based steering approach for crowd simulation

Authors:

Julien Pettré,

Anne-Hélène Olivier,

Stéphane DonikianAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 29, Issue 4

Article No.: 123, Pages 1 - 9

https://doi.org/10.1145/1778765.1778860

Published: 26 July 2010 Publication History

Abstract

In the everyday exercise of controlling their locomotion, humans rely on their optic flow of the perceived environment to achieve collision-free navigation. In crowds, in spite of the complexity of the environment made of numerous obstacles, humans demonstrate remarkable capacities in avoiding collisions. Cognitive science work on human locomotion states that relatively succinct information is extracted from the optic flow to achieve safe locomotion. In this paper, we explore a novel vision-based approach of collision avoidance between walkers that fits the requirements of interactive crowd simulation. By simulating humans based on cognitive science results, we detect future collisions as well as the level of danger from visual stimuli. The motor-response is twofold: a reorientation strategy prevents future collision, whereas a deceleration strategy prevents imminent collisions. Several examples of our simulation results show that the emergence of self-organized patterns of walkers is reinforced using our approach. The emergent phenomena are visually appealing. More importantly, they improve the overall efficiency of the walkers' traffic and avoid improbable locking situations.

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Cited By

BARUT Ö(2024)Kalabalık Benzetimlerinde Küçük Gruplar için GPU Tabanlı Çarpışmasız Doğrusal Gezinge OluşturulmasıGPU-Based Collision-Free Linear Trajectory Generation for Small Groups in Crowd SimulationsPoliteknik Dergisi10.2339/politeknik.140900627:1(407-417)Online publication date: 29-Feb-2024
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Itatani RPelechano N(2024)Social Crowd Simulation: Improving Realism with Social Rules and Gaze BehaviorProceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games10.1145/3677388.3696337(1-11)Online publication date: 21-Nov-2024
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Ferreira DShatzel LHaworth B(2024)Deformable Elliptical Particles for Predictive Mesh-Adaptive CrowdsProceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games10.1145/3677388.3696329(1-11)Online publication date: 21-Nov-2024
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Index Terms

A synthetic-vision based steering approach for crowd simulation
1. Computing methodologies
  1. Computer graphics
    1. Animation
  2. Modeling and simulation
    1. Simulation types and techniques
      1. Simulation by animation

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 29, Issue 4

July 2010

942 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1778765

Issue’s Table of Contents

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 July 2010

Published in TOG Volume 29, Issue 4

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Cited By

BARUT Ö(2024)Kalabalık Benzetimlerinde Küçük Gruplar için GPU Tabanlı Çarpışmasız Doğrusal Gezinge OluşturulmasıGPU-Based Collision-Free Linear Trajectory Generation for Small Groups in Crowd SimulationsPoliteknik Dergisi10.2339/politeknik.140900627:1(407-417)Online publication date: 29-Feb-2024
https://doi.org/10.2339/politeknik.1409006
Itatani RPelechano N(2024)Social Crowd Simulation: Improving Realism with Social Rules and Gaze BehaviorProceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games10.1145/3677388.3696337(1-11)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3677388.3696337
Ferreira DShatzel LHaworth B(2024)Deformable Elliptical Particles for Predictive Mesh-Adaptive CrowdsProceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games10.1145/3677388.3696329(1-11)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3677388.3696329
Zhou YLai PYu JXiong YYang HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Hydrodynamics-Informed Neural Network for Simulating Dense Crowd Motion PatternsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681277(4553-4561)Online publication date: 28-Oct-2024
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Yin THoyet LChristie MCani MPettré J(2024)With or Without You: Effect of Contextual and Responsive Crowds on VR-based Crowd Motion CaptureIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337203830:5(2785-2795)Online publication date: 4-Mar-2024
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Yu BYe JOu D(2024)Consideration of Human Vision in Crowd SimulationsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340177425:10(13364-13374)Online publication date: 1-Oct-2024
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Iyer PNegi RSchadschneider AGompper G(2024)Directed motion of cognitive active agents in a crowded three-way intersectionCommunications Physics10.1038/s42005-024-01860-x7:1Online publication date: 22-Nov-2024
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