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多用户眼动跟踪数据的可视化共享与协同交互
引用本文:程时伟,沈哓权,孙凌云,胡屹凛.多用户眼动跟踪数据的可视化共享与协同交互[J].软件学报,2019,30(10):3037-3053.
作者姓名:程时伟  沈哓权  孙凌云  胡屹凛
作者单位:浙江工业大学 计算机科学与技术学院, 浙江 杭州 310023,浙江工业大学 计算机科学与技术学院, 浙江 杭州 310023,计算机辅助设计与图形学国家重点实验室(浙江大学), 浙江 杭州 310058,浙江工业大学 计算机科学与技术学院, 浙江 杭州 310023
基金项目:国家重点研发计划(2016YFB1001403);国家自然科学基金(61772468,61672451)
摘    要:随着数字图像处理技术的发展,以及计算机支持的协同工作研究的深入,眼动跟踪开始应用于多用户协同交互.但是已有的眼动跟踪技术主要针对单个用户,多用户眼动跟踪计算架构不成熟、标定过程复杂,眼动跟踪数据的记录、传输以及可视化共享机制都有待深入研究.为此,建立了基于梯度优化的协同标定模型,简化多用户的眼动跟踪标定过程;然后提出面向多用户的眼动跟踪计算架构,优化眼动跟踪数据的传输和管理.进一步地,探索眼动跟踪数据的可视化形式在协同交互环境下对用户视觉注意行为的影响,具体设计了圆点、散点、轨迹这3种可视化形式,并验证了圆点形式能够有效地提高多用户协同搜索任务的完成效率.在此基础上,设计与开发了基于眼动跟踪的代码协同审查系统,实现了代码审查过程中多用户眼动跟踪数据的同步记录、分发,以及基于实时注视点、代码行边框和背景灰度、代码行之间连线的可视化共享.用户实验结果表明,代码错误的平均搜索时间比没有眼动跟踪数据可视化分享时减少了20.1%,显著提高了协同工作效率,验证了该方法的有效性.

关 键 词:眼动跟踪  计算机支持的协同工作  视觉认知  人机交互  信息可视化
收稿时间:2018/8/18 0:00:00
修稿时间:2018/11/1 0:00:00

Shared Visualization and Collaborative Interaction Based on Multiple User Eye Tracking Data
CHENG Shi-Wei,SHEN Xiao-Quan,SUN Ling-Yun and HU Yi-Lin.Shared Visualization and Collaborative Interaction Based on Multiple User Eye Tracking Data[J].Journal of Software,2019,30(10):3037-3053.
Authors:CHENG Shi-Wei  SHEN Xiao-Quan  SUN Ling-Yun and HU Yi-Lin
Affiliation:School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China,School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China,State Key Laboratory of CAD & CG(Zhejiang University), Hangzhou 310058, China and School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Abstract:With the development of digital image processing technology and computer supported cooperative work, eye tracking has been applied in the process of multiuser collaborative interaction. However, existed eye tracking technique can only track single user''s gaze, and the computing framework for multiple user''s gaze data tracking is not mature; besides, the calibration process is much complex, and the eye tracking data recording, transition, and visualization mechanisms need to be further explored. Hence, this study proposed a new collaborative calibration method based on gradient optimization algorithms, so as to simplify the calibration process; and then in order to optimize the eye tracking data transition and management, the computing framework oriented to multiple user''s eye tracking is proposed. Furthermore, to explore the influence of visual attention caused by visualization of eye tracking data sharing among multiple users, visualizations such as dots, clusters and trajectories are designed, and it is validated that the dots could improve the efficiency for collaborative visual search tasks. Finally, the code collaborative review systems are designed and built based on eye tracking, and this system could record, deliver, and visualize the eye tracking data in the forms of dots, code borders, code background, lines connected codes, among the code reviewing process. The user experiment result shows that, compared to the no eye tracking data sharing condition, sharing eye tracking data among multiple users can reduce the bug searching time with 20.1%, significantly improves the efficiency of collaborative work, and it validates the effectiveness of the proposed approach.
Keywords:eye tracking  computer supported cooperative work  visual cognition  human-computer interaction  information visualization
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