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2020 | OriginalPaper | Buchkapitel

Hierarchical HMM for Eye Movement Classification

verfasst von : Ye Zhu, Yan Yan, Oleg Komogortsev

Erschienen in: Computer Vision – ECCV 2020 Workshops

Verlag: Springer International Publishing

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Abstract

In this work, we tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data. The efficient classification of these different types of eye movements helps to better analyze and utilize the eye tracking data. Different from the existing methods that detect eye movement by several pre-defined threshold values, we propose a hierarchical Hidden Markov Model (HMM) statistical algorithm for detecting fixations, saccades and smooth pursuits. The proposed algorithm leverages different features from the recorded raw eye tracking data with a hierarchical classification strategy, separating one type of eye movement each time. Experimental results demonstrate the effectiveness and robustness of the proposed method by achieving competitive or better performance compared to the state-of-the-art methods.

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Vorheriges Kapitel Efficiency in Real-Time Webcam Gaze Tracking

Nächstes Kapitel Domain Adaptation for Eye Segmentation

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Titel: Hierarchical HMM for Eye Movement Classification
verfasst von: Ye Zhu
Yan Yan
Oleg Komogortsev
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2020 Workshops
Print ISBN: 978-3-030-66414-5

Electronic ISBN: 978-3-030-66415-2

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-66415-2_35

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