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

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.-L. (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
• Public URL: https://durham-repository.worktribe.com/output/1215693