abstract

An Eye-Tracking Dataset for Visual Attention Modelling in a Virtual Museum Context

Authors:

Henry B.L. DuhAuthors Info & Claims

VRCAI '19: Proceedings of the 17th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry

Article No.: 39, Pages 1 - 3

https://doi.org/10.1145/3359997.3365738

Published: 14 November 2019 Publication History

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Abstract

Predicting the user’s visual attention enables a virtual reality (VR) environment to provide a context-aware and interactive user experience. Researchers have attempted to understand visual attention using eye-tracking data in a 2D plane. In this poster, we propose the first 3D eye-tracking dataset for visual attention modelling in the context of a virtual museum. It comprises about 7 million records and may facilitate visual attention modelling in a 3D VR space.

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Zhou YFeng TShuai SLi XSun LDuh H(2022)EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museumEDVAM:用于虚拟博物馆视觉注意建模的三维眼动数据集Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.200031823:1(101-112)Online publication date: 6-Feb-2022
https://doi.org/10.1631/FITEE.2000318
Li XShan YChen WWu YHansen PPerrault S(2021)Predicting user visual attention in virtual reality with a deep learning modelVirtual Reality10.1007/s10055-021-00512-725:4(1123-1136)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s10055-021-00512-7

Index Terms

An Eye-Tracking Dataset for Visual Attention Modelling in a Virtual Museum Context
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Virtual reality
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
    2. Interaction paradigms
      1. Mixed / augmented reality
      2. Virtual reality
  2. Visualization
    1. Visualization design and evaluation methods
    2. Visualization systems and tools

Index terms have been assigned to the content through auto-classification.

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Published In

VRCAI '19: Proceedings of the 17th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry

November 2019

354 pages

ISBN:9781450370028

DOI:10.1145/3359997

Conference Chairs:
Joaquim Jorge
INESC/ID & Univ Lisboa, Portugal
,
June Kim
University of New South Wales, Australia
,
Herman Van Eyken
Griffith University, Queensland, Australia
,
Editor:
Stephen N. Spencer
University of Washington
,
Program Chairs:
Mashhuda Glencross
University of Queensland, Australia
,
Kenny Mitchell
Disney Research, USA and Edinburgh Napier University, UK
,
Takuji Narumi
University of Tokyo, Japan

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 14 November 2019

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SIGGRAPH

VRCAI '19: The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry

November 14 - 16, 2019

QLD, Brisbane, Australia

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Zhou YFeng TShuai SLi XSun LDuh H(2022)EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museumEDVAM:用于虚拟博物馆视觉注意建模的三维眼动数据集Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.200031823:1(101-112)Online publication date: 6-Feb-2022
https://doi.org/10.1631/FITEE.2000318
Li XShan YChen WWu YHansen PPerrault S(2021)Predicting user visual attention in virtual reality with a deep learning modelVirtual Reality10.1007/s10055-021-00512-725:4(1123-1136)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s10055-021-00512-7

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Abstract

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Recommendations

A Task-Driven Eye Tracking Dataset for Visual Attention Analysis

Assessing the Spatial Distribution of Visual Attention in a Virtual Environment: Development and Validation of a Novel VR-based Attentional Visual Field (AVF) Task

Measuring Visual Attention Capacity Across xReality

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