Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest

Abstract

Eye tracking is used to analyze and compare user behaviour across diverse domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene, while OoI identification uncovers distinct objects in the scene that attract user attention. Using scalable clustering and cluster merging that is not constrained by input parameters, we label AoIs across multiple users in long duration eye tracking experiments. Using the common AoI labels then allows direct comparison of the users as well as the use of such methods as Hidden Markov Models and Sequence mining to uncover interesting behaviour across the users which, until now, has been prohibitively difficult to achieve.