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

作     者：Yunzhan ZHOU Tian FENG Shihui SHUAI Xiangdong LI Lingyun SUN Henry Been-Lirn DUH Yunzhan ZHOU;Tian FENG;Shihui SHUAI;Xiangdong LI;Lingyun SUN;Henry Been-Lirn DUH

作者机构：Department of Computer ScienceDurham UniversityDurhamDH13LEUK Department of Computer Science and Information TechnologyLa Trobe UniversityMelbourneVIC3086Australia Tian Feng&Henry Been-Lirn Duh Alibaba GroupHangzhou311121China Department of Digital MediaZhejiang UniversityHangzhou310027China International Design InstituteZhejiang UniversityHangzhou310058China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2022年第23卷第1期

页      面：101-112页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 0303[法学-社会学] 0710[理学-生物学] 0601[历史学-考古学] 1301[艺术学-艺术学理论] 06[历史学] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：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)。 

主　　题：Visual attention Virtual museums Eye-tracking datasets Gaze detection Deep learning 

摘      要：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.