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

2022, Volume 23, Issue 1

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Frontiers of Information Technology & Electronic Engineering >> 2022, Volume 23, Issue 1 doi: 10.1631/FITEE.2000318

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

Affiliation(s): Department of Computer Science, Durham University, Durham DH1 3LE, UK; Department of Computer Science and Information Technology, La Trobe University, VIC 3086, Australia; Alibaba Group, Hangzhou 311121, China; Department of Digital Media, Zhejiang University, Hangzhou 310027, China; International Design Institute, Zhejiang University, Hangzhou 310058, China; less

Received: 2020-07-03 Accepted: 2022-01-24 Available online: 2022-01-24

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Abstract

Predicting facilitates an adaptive virtual museum environment and provides a context-aware and interactive user experience. Explorations toward development of a 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 modeling in a virtual Museum, known as the EDVAM. In addition, a model is devised and tested with the EDVAM to predict a user's subsequent from previous eye movements. This work provides a reference for modeling and context-aware interaction in the context of .

Keywords

Visual attention ; Virtual museums ; Eye-tracking datasets ; Gaze detection ; Deep learning

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