Shared Visualization and Collaborative Interaction Based on Multiple User Eye Tracking Data

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. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.