VETA: Visual eye-tracking analytics for the exploration of gaze patterns and behaviours

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Visual Informatics Pub Date : 2022-06-01 DOI:10.1016/j.visinf.2022.02.004
Sarah Goodwin , Arnaud Prouzeau , Ryan Whitelock-Jones , Christophe Hurter , Lee Lawrence , Umair Afzal , Tim Dwyer
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引用次数: 7

Abstract

Eye tracking is growing in popularity for multiple application areas, yet analysing and exploring the large volume of complex data remains difficult for most users. We present a comprehensive eye tracking visual analytics system to enable the exploration and presentation of eye-tracking data across time and space in an efficient manner. The application allows the user to gain an overview of general patterns and perform deep visual analysis of local gaze exploration. The ability to link directly to the video of the underlying scene allows the visualisation insights to be verified on the fly. The system was motivated by the need to analyse eye-tracking data collected from an ‘in the wild’ study with energy network operators and has been further evaluated via interviews with 14 eye-tracking experts in multiple domains. Results suggest that, thanks to state-of-the-art visualisation techniques and by providing context with videos, our system could enable an improved analysis of eye-tracking data through interactive exploration, facilitating comparison between different participants or conditions, thus enhancing the presentation of complex data analysis to non-experts. This research paper provides four contributions: (1) analysis of a motivational use case demonstrating the need for rich visual-analytics workflow tools for eye-tracking data; (2) a highly dynamic system to visually explore and present complex eye-tracking data; (3) insights from our applied use case evaluation and interviews with experienced users demonstrating the potential for the system and visual analytics for the wider eye-tracking community.

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VETA:用于探索凝视模式和行为的视觉眼动追踪分析
眼动追踪在多个应用领域越来越受欢迎,但对大多数用户来说,分析和探索大量复杂数据仍然很困难。我们提出了一个全面的眼动追踪视觉分析系统,使眼动追踪数据能够跨时间和空间高效地探索和呈现。该应用程序允许用户获得一般模式的概述,并对局部凝视探索进行深入的视觉分析。直接链接到底层场景视频的能力使得可视化见解能够在飞行中得到验证。该系统的动机是需要分析从能源网络运营商的“野外”研究中收集的眼球追踪数据,并通过对14位多个领域的眼球追踪专家的采访进行进一步评估。结果表明,得益于最先进的可视化技术和提供视频背景,我们的系统可以通过交互式探索改进眼动追踪数据的分析,促进不同参与者或条件之间的比较,从而增强对非专家的复杂数据分析的呈现。本研究论文提供了四个贡献:(1)分析了一个动机用例,表明需要丰富的视觉分析工作流工具来处理眼动追踪数据;(2)高动态系统,以视觉方式探索和呈现复杂的眼动追踪数据;(3)从我们的应用用例评估和与经验丰富的用户的访谈中得出的见解,展示了系统和视觉分析在更广泛的眼动追踪社区中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
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