Paper Info
Title
Space-time visual analytics of eye-tracking data for dynamic stimuli.
Abstract
We introduce a visual analytics method to analyze eye movement data recorded for dynamic stimuli such as video or animated graphics. The focus lies on the analysis of data of several viewers to identify trends in the general viewing behavior, including time sequences of attentional synchrony and objects with strong attentional focus. By using a space-time cube visualization in combination with clustering, the dynamic stimuli and associated eye gazes can be analyzed in a static 3D representation. Shotbased, spatiotemporal clustering of the data generates potential areas of interest that can be filtered interactively. We also facilitate data drill-down: the gaze points are shown with density-based color mapping and individual scan paths as lines in the space-time cube. The analytical process is supported by multiple coordinated views that allow the user to focus on different aspects of spatial and temporal information in eye gaze data. Common eye-tracking visualization techniques are extended to incorporate the spatiotemporal characteristics of the data. For example, heat maps are extended to motion-compensated heat maps and trajectories of scan paths are included in the space-time visualization. Our visual analytics approach is assessed in a qualitative users study with expert users, which showed the usefulness of the approach and uncovered that the experts applied different analysis strategies supported by the system.
Year
DOI
Venue
2013
10.1109/TVCG.2013.194
IEEE Trans. Vis. Comput. Graph.
Keywords
Field
DocType
data drill-down,motion-compensated heat map,viewing behavior,pattern clustering,scan path trajectory,space-time visualization,individual scan paths,animated graphics,eye-tracking data,spatial information,data analysis,eye gaze,motion-compensated heat maps,temporal information,dynamic stimulus,common eye-tracking visualization technique,eye-tracking visualization techniques,spatiotemporal phenomena,spatiotemporal clustering,space-time visual analytics method,eye movement data analysis,density-based color mapping,dynamic areas of interest,space-time visual analytics,context awareness,data visualisation,visual analytics,video,tracking,associated eye gaze,eye-tracking,clustering algorithms,attentional synchrony,strong attentional focus,data visualization,space-time codes,eye movement data,attentional focus,space-time cube visualization,space-time cube,attentional synchrony time sequence,spatiotemporal data clustering,dynamic stimuli,static 3d representation,image motion analysis,eye tracking
Computer vision,Data visualization,Data analysis,Computer science,Visualization,Visual analytics,Eye tracking,Eye movement,Artificial intelligence,Cluster analysis,Creative visualization
Journal
Volume
Issue
ISSN
19
12
1941-0506
Citations 
PageRank 
References 
25
0.92
24
Authors
2
Name
Order
Citations
PageRank
Kuno Kurzhals122720.63
Daniel Weiskopf22988204.30