Bayesian online clustering of eye movement data

Authors:
Enkelejda Tafaj;Gjergji Kasneci;Wolfgang Rosenstiel;Martin Bogdan
Affiliations:
University of Tübingen, Germany;Hasso-Plattner-Institute, Germany;University of Tübingen, Germany;University of Leipzig, Germany
Venue:
Proceedings of the Symposium on Eye Tracking Research and Applications
Year:
2012

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Abstract

The task of automatically tracking the visual attention in dynamic visual scenes is highly challenging. To approach it, we propose a Bayesian online learning algorithm. As the visual scene changes and new objects appear, based on a mixture model, the algorithm can identify and tell visual saccades (transitions) from visual fixation clusters (regions of interest). The approach is evaluated on real-world data, collected from eye-tracking experiments in driving sessions.