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