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Study of Polynomial Mapping Functions in Video-Oculography Eye Trackers

Authors:

Juan J. Cerrolaza,

Arantxa Villanueva,

Rafael CabezaAuthors Info & Claims

ACM Transactions on Computer-Human Interaction (TOCHI), Volume 19, Issue 2

Article No.: 10, Pages 1 - 25

https://doi.org/10.1145/2240156.2240158

Published: 01 July 2012 Publication History

Abstract

Gaze-tracking data have been used successfully in the design of new input devices and as an observational technique in usability studies. Polynomial-based Video-Oculography (VOG) systems are one of the most attractive gaze estimation methods thanks to their simplicity and ease of implementation. Although the functionality of these systems is generally acceptable, there has been no thorough comparative study to date of how the mapping equations affect the final system response. After developing a taxonomic classification of calibration functions, we examined over 400,000 models and evaluated the validity of several conventional assumptions. Our rigorous experimental procedure enabled us to optimize the calibration process for a real VOG gaze-tracking system and halve the calibration time while avoiding a detrimental effect on the accuracy or tolerance to head movement. Finally, a geometry-based method is implemented and tested. The results and performance is compared with those obtained by the general purpose expressions.

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Study of Polynomial Mapping Functions in Video-Oculography Eye Trackers

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Published In

cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 19, Issue 2

July 2012

226 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/2240156

Issue’s Table of Contents

Copyright © 2012 ACM.

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Publication History

Published: 01 July 2012

Accepted: 01 November 2011

Revised: 01 November 2011

Received: 01 July 2011

Published in TOCHI Volume 19, Issue 2

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Cited By

Niehorster DNyström MHessels RAndersson RBenjamins JHansen DHooge I(2025)The fundamentals of eye tracking part 4: Tools for conducting an eye tracking studyBehavior Research Methods10.3758/s13428-024-02529-757:1Online publication date: 6-Jan-2025
https://doi.org/10.3758/s13428-024-02529-7
Wan ZZhang HYang MWu QWu S(2024)Self-Calibrating Gaze Estimation via Matching Spatio-Temporal Reading Patterns and Eye-Feature PatternsIEEE Transactions on Industrial Informatics10.1109/TII.2024.341329220:10(11697-11707)Online publication date: Oct-2024
https://doi.org/10.1109/TII.2024.3413292
Zhang WCao JWang XXia PLi BChen X(2024)Eyeball Kinematics Informed Slippage Robust Gaze TrackingIEEE Sensors Journal10.1109/JSEN.2024.347500924:22(37620-37629)Online publication date: 15-Nov-2024
https://doi.org/10.1109/JSEN.2024.3475009
Rui ZLi YDong ZHao LChen BChang FGu Z(2024)EEG, EOG, Likert Scale, and Interview Approaches for Assessing Stressful Hazard Perception ScenariosInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2358461(1-26)Online publication date: 3-Jun-2024
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