Benchmark data for evaluating visualization and analysis techniques for eye tracking for video stimuli

For the analysis of eye movement data, an increasing number of analysis methods have emerged to examine and analyze different aspects of the data. In particular, due to the complex spatio-temporal nature of gaze data for dynamic stimuli, there has been a need and recent trend toward the development of visualization and visual analytics techniques for such data. With this paper, we provide benchmark data to test visualization and visual analytics methods, but also other analysis techniques for gaze processing. In particular, for eye tracking data from video stimuli, existing datasets often provide few information about recorded eye movement patterns and, therefore, are not comprehensive enough to allow for a faithful assessment of the analysis methods. Our benchmark data consists of three ingredients: the dynamic stimuli in the form of video, the eye tracking data, and annotated areas of interest. We designed the video stimuli and the tasks for the participants of the eye tracking experiments in a way to trigger typical viewing patterns, including attentional synchrony, smooth pursuit, and switching of the focus of attention. In total, we created 11 videos with eye tracking data acquired from 25 participants.