@inproceedings{52f78e19ac9f474ea3350c9c025e0502,
title = "The applicability of probabilistic methods to the online recognition of fixations and saccades in dynamic scenes",
abstract = "In many applications involving scanpath analysis, especially when dynamic scenes are viewed, consecutive fixations and saccades, have to be identified and extracted from raw eye-tracking data in an online fashion. Since probabilistic methods can adapt not only to the individual viewing behavior, but also to changes in the scene, they are best suited for such tasks. In this paper we analyze the applicability of two types of mainstream probabilistic models to the identification of fixations and saccades in dynamic scenes: (1) Hidden Markov Models and (2) Bayesian Online Mixture Models. We analyze and compare the classification performance of the models on eye-tracking data collected during real-world driving experiments.",
keywords = "Classification, Dynamic scene, Eye movements, Eye tracking, Models, Online, Probabilistic",
author = "Enkelejda Kasneci and Gjergji Kasneci and K{\"u}bler, {Thomas C.} and Wolfgang Rosenstiel",
year = "2014",
doi = "10.1145/2578153.2578213",
language = "English",
isbn = "9781450327510",
series = "Eye Tracking Research and Applications Symposium (ETRA)",
publisher = "Association for Computing Machinery",
pages = "323--326",
booktitle = "Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA 2014",
note = "8th Symposium on Eye Tracking Research and Applications, ETRA 2014 ; Conference date: 26-03-2014 Through 28-03-2014",
}