@inproceedings{b6d1abe86b5b46de9290fbd5d744ffc4,
title = "Bayesian online clustering of eye movement data",
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.",
keywords = "Bayesian model, eye movement data, fixation clusters, online clustering",
author = "Enkelejda Tafaj and Gjergji Kasneci and Wolfgang Rosenstiel and Martin Bogdan",
year = "2012",
month = mar,
day = "28",
doi = "10.1145/2168556.2168617",
language = "English",
isbn = "9781450312257",
series = "Eye Tracking Research and Applications Symposium (ETRA)",
publisher = "Association for Computing Machinery",
pages = "285--288",
booktitle = "Proceedings - ETRA 2012",
note = "7th Eye Tracking Research and Applications Symposium, ETRA 2012 ; Conference date: 28-03-2012 Through 30-03-2012",
}