Algorithmic gaze classification for mobile eye-Tracking

Abstract

Mobile eye tracking traditionally requires gaze to be coded manually. We introduce an open-source Python package (GazeClassify) that algorithmically annotates mobile eye tracking data for the study of human interactions. Instead of manually identifying objects and identifying if gaze is directed towards an area of interest, computer vision algorithms are used for the identification and segmentation of human bodies. To validate the algorithm, mobile eye tracking data from short combat sport sequences were analyzed. The performance of the algorithm was compared against three manual raters. The algorithm performed with substantial reliability in comparison to the manual raters when it came to annotating which area of interest gaze was closest to. However, the algorithm was more conservative than the manual raters for classifying if gaze was directed towards an object of interest. The algorithmic approach represents a viable and promising means for automating gaze classification for mobile eye tracking.