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SeTA: semiautomatic tool for annotation of eye tracking images

Authors:

Andoni Larumbe-Bergera,

Sonia Porta,

Rafael Cabeza,

Arantxa VillanuevaAuthors Info & Claims

ETRA '19: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications

Article No.: 45, Pages 1 - 5

https://doi.org/10.1145/3314111.3319830

Published: 25 June 2019 Publication History

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Abstract

Availability of large scale tagged datasets is a must in the field of deep learning applied to the eye tracking challenge. In this paper, the potential of Supervised-Descent-Method (SDM) as a semiautomatic labelling tool for eye tracking images is shown. The objective of the paper is to evidence how the human effort needed for manually labelling large eye tracking datasets can be radically reduced by the use of cascaded regressors. Different applications are provided in the fields of high and low resolution systems. An iris/pupil center labelling is shown as example for low resolution images while a pupil contour points detection is demonstrated in high resolution. In both cases manual annotation requirements are drastically reduced.

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Han DAziere NWang TOssmy OKrishna AWang HShen RTodorovic SAdolph K(2024)Infants' Developing Environment: Integration of Computer Vision and Human Annotation to Quantify where Infants Go, what They Touch, and what They See2024 IEEE International Conference on Development and Learning (ICDL)10.1109/ICDL61372.2024.10644441(1-8)Online publication date: 20-May-2024
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Larumbe-Bergera AGarde GPorta SCabeza RVillanueva A(2021)Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural NetworksSensors10.3390/s2120684721:20(6847)Online publication date: 15-Oct-2021
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Index Terms

SeTA: semiautomatic tool for annotation of eye tracking images
1. Applied computing
  1. Document management and text processing
    1. Document preparation
      1. Annotation
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression

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Information & Contributors

Information

Published In

ETRA '19: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications

June 2019

623 pages

ISBN:9781450367097

DOI:10.1145/3314111

Conference Chairs:
Krzysztof Krejtz
SWPS University, Poland
,
Bonita Sharif
University of Nebraska-Lincoln

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2019

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ETRA '19

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ETRA '19: 2019 Symposium on Eye Tracking Research and Applications

June 25 - 28, 2019

Colorado, Denver

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Overall Acceptance Rate 69 of 137 submissions, 50%

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Antwi-Bekoe ETietaa Maale GMensah Martey EAsiedu WNyame GFrimpong Nyamaah E(2024)Data Readiness and Data Exploration for Successful Power Line InspectionDeep Learning - Recent Findings and Research10.5772/intechopen.112637Online publication date: 29-May-2024
https://doi.org/10.5772/intechopen.112637
Han DAziere NWang TOssmy OKrishna AWang HShen RTodorovic SAdolph K(2024)Infants' Developing Environment: Integration of Computer Vision and Human Annotation to Quantify where Infants Go, what They Touch, and what They See2024 IEEE International Conference on Development and Learning (ICDL)10.1109/ICDL61372.2024.10644441(1-8)Online publication date: 20-May-2024
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Larumbe-Bergera AGarde GPorta SCabeza RVillanueva A(2021)Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural NetworksSensors10.3390/s2120684721:20(6847)Online publication date: 15-Oct-2021
https://doi.org/10.3390/s21206847
Sager CJaniesch CZschech P(2021)A survey of image labelling for computer vision applicationsJournal of Business Analytics10.1080/2573234X.2021.19088614:2(91-110)Online publication date: 18-Apr-2021
https://doi.org/10.1080/2573234X.2021.1908861
Porta SBossavit BCabeza RLarumbe-Bergera AGarde GVillanueva A(2019)U2Eyes: A Binocular Dataset for Eye Tracking and Gaze Estimation2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)10.1109/ICCVW.2019.00451(3660-3664)Online publication date: Oct-2019
https://doi.org/10.1109/ICCVW.2019.00451

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