Evaluation of classification algorithms for smooth pursuit eye movements : Evaluating current algorithms for smooth pursuit detection on Tobii Eye Trackers

Abstract: Eye tracking is a field that has been growing immensely over the last decade. Accompanying this growth is a need for simplified and automatic analysis of eye tracking data. A part of that analysis is eye movement classification, and while there are many adequate classification methods for fixations and saccades, the tools for smooth pursuit classification are still lacking. This thesis gives an overview of the field,and analyses five different methods for classifying smooth pursuits, fixations,and saccades. The analysis also explores evaluation methods that avoid the laborious way of manually tagging data to get a reference classification. Despite earlier reports of decent performance, the overall results for all the analysed algorithms is poor. In particular, the slowest pursuits are consistently misclassified. Most certainly, the inclusion of the slow pursuits have skewed the results, but even disregarding them doesn’t yield particularly impressive results. This begs the question of what concessions one has to make in terms of prerequisites on the data, or qualifiers for the resulting analysis, to achieve adequate performance,and given those, when would such a classification be preferred to something tailored to the problem at hand?