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.

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

Department of Computer Science,Durham University,Durham DH13LE,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

信息与电子工程前沿（英文）

[1]Yunzhan ZHOU;Tian FENG;Shihui SHUAI;Xiangdong LI;Lingyun SUN;Henry Been-Lirn DUH-.EDVAM:a 3D eye-tracking dataset for visual attention modeling in a virtual museum)[J].信息与电子工程前沿（英文）,2022(01):101-112

EDVAM,Explorations

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