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EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum

Zhou, Yunzhan; Feng, Tian; Shuai, Shihui; Li, Xiangdong; Sun, Lingyun; Duh, Henry Been-Lirn

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Authors

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Yunzhan Zhou yunzhan.zhou@durham.ac.uk
PGR Student Doctor of Philosophy

Tian Feng

Shihui Shuai

Xiangdong Li

Lingyun Sun

Henry Been-Lirn Duh



Abstract

Predicting visual attention facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience. Explorations toward development of a visual attention mechanism using eye-tracking data have so far been limited to 2D cases, and researchers are yet to approach this topic in a 3D virtual environment and from a spatiotemporal perspective. We present the first 3D Eye-tracking Dataset for Visual Attention modeling in a virtual Museum, known as the EDVAM. In addition, a deep learning model is devised and tested with the EDVAM to predict a user’s subsequent visual attention from previous eye movements. This work provides a reference for visual attention modeling and context-aware interaction in the context of virtual museums.

Citation

Zhou, Y., Feng, T., Shuai, S., Li, X., Sun, L., & Duh, H. B. (2022). EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum. Frontiers of Information Technology & Electronic Engineering, 23(1), 101-112. https://doi.org/10.1631/fitee.2000318

Journal Article Type Article
Acceptance Date Feb 15, 2021
Online Publication Date Feb 6, 2022
Publication Date 2022-01
Deposit Date Mar 10, 2022
Publicly Available Date Feb 6, 2023
Journal Frontiers of Information Technology & Electronic Engineering
Electronic ISSN 2095-9230
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 23
Issue 1
Pages 101-112
DOI https://doi.org/10.1631/fitee.2000318

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Copyright Statement
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1631/FITEE.2000318




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