EDVAM:用于虚拟博物馆视觉注意建模的三维眼动数据集

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

doi:10.1631/FITEE.2000318

EDVAM:用于虚拟博物馆视觉注意建模的三维眼动数据集

Translated title of the contribution: EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum

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

Research output: Journal article publication › Journal article › Academic research › peer-review

12 Citations (Scopus)

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.

Translated title of the contribution	EDVAM: a 3D eye-tracking dataset for visual attention modeling in a virtual museum
Original language	Chinese (Simplified)
Pages (from-to)	101-112
Number of pages	12
Journal	Frontiers of Information Technology and Electronic Engineering
Volume	23
Issue number	1
DOIs	https://doi.org/10.1631/FITEE.2000318
Publication status	Published - Jan 2022
Externally published	Yes

Keywords

Deep learning
Eye-tracking datasets
Gaze detection
TP391
Virtual museums
Visual attention

ASJC Scopus subject areas

Signal Processing
Hardware and Architecture
Computer Networks and Communications
Electrical and Electronic Engineering

More information

10.1631/FITEE.2000318

Cite this

@article{707c4797e3d941d6bf672195cfc66cc1,

title = "EDVAM:用于虚拟博物馆视觉注意建模的三维眼动数据集",

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{\textquoteright}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.",

keywords = "Deep learning, Eye-tracking datasets, Gaze detection, TP391, Virtual museums, Visual attention",

author = "Yunzhan Zhou and Tian Feng and Shihui Shuai and Xiangdong Li and Lingyun Sun and Duh, \{Henry Been Lirn\}",

note = "Funding Information: Project supported by the National Natural Science Foundation of China (No. 61802341), the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China (No. 2018AAA0100703), the Research Innovation Plan of the Ministry of Education of China, and the Provincial Key Research and Development Plan of Zhejiang Province, China (No. 2019C03137) Publisher Copyright: {\textcopyright} 2022, Zhejiang University Press.",

year = "2022",

month = jan,

doi = "10.1631/FITEE.2000318",

language = "Chinese (Simplified)",

volume = "23",

pages = "101--112",

journal = "Frontiers of Information Technology and Electronic Engineering",

issn = "2095-9184",

publisher = "Zhejiang University",

number = "1",

}

TY - JOUR

T1 - EDVAM:用于虚拟博物馆视觉注意建模的三维眼动数据集

AU - Zhou, Yunzhan

AU - Feng, Tian

AU - Shuai, Shihui

AU - Li, Xiangdong

AU - Sun, Lingyun

AU - Duh, Henry Been Lirn

N1 - Funding Information: Project supported by the National Natural Science Foundation of China (No. 61802341), the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China (No. 2018AAA0100703), the Research Innovation Plan of the Ministry of Education of China, and the Provincial Key Research and Development Plan of Zhejiang Province, China (No. 2019C03137) Publisher Copyright: © 2022, Zhejiang University Press.

PY - 2022/1

Y1 - 2022/1

N2 - 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.

AB - 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.

KW - Deep learning

KW - Eye-tracking datasets

KW - Gaze detection

KW - TP391

KW - Virtual museums

KW - Visual attention

UR - https://www.scopus.com/pages/publications/85124493879

U2 - 10.1631/FITEE.2000318

DO - 10.1631/FITEE.2000318

M3 - Journal article

AN - SCOPUS:85124493879

SN - 2095-9184

VL - 23

SP - 101

EP - 112

JO - Frontiers of Information Technology and Electronic Engineering

JF - Frontiers of Information Technology and Electronic Engineering

IS - 1

ER -

EDVAM:用于虚拟博物馆视觉注意建模的三维眼动数据集

Abstract

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this