Image-Based Projection Labeling for Mobile Eye Tracking

Abstract

The annotation of gaze data concerning investigated areas of interest (AOIs) poses a time-consuming step in the analysis procedure of eye tracking experiments. For data from mobile eye tracking glasses, the annotation effort is further increased because each recording has to be investigated individually. Automated approaches based on supervised machine learning require pre-trained categories which are hard to obtain without human interpretation, i.e., labeling ground truth data. We present an interactive visualization approach that supports efficient annotation of gaze data based on image content participants with eye tracking glasses focused on. Recordings can be segmented individually to reduce the annotation effort. Thumbnails represent segments visually and are projected on a 2D plane for a fast comparison of AOIs. Annotated scanpaths can then be interpreted directly with the timeline visualization. We showcase our approach with three different scenarios.