Panoptic Visual Analytics of Eye Tracking Data

In eye tracking data visualization, areas of interest (AOIs) are widely adopted to analyze specific regions of the stimulus. We propose a visual analytics tool that leverages panoptic segmentation to automatically divide the whole image or frame video in semantic AOIs. A set of AOI-based visualization techniques are available to analyze the fixation data based on these semantic AOIs. Moreover, we propose a modified version of radial transition graph visualizations adapted to the extracted semantic AOIs and a new visualization technique also based on radial transition graphs. Two application examples illustrate the potential of this approach and are used to discuss its usefulness and limitations.