research-article

Hybrid eye detection algorithm for outdoor environments

Authors:

Jose Javier Bengoechea,

Arantxa Villanueva,

Rafael CabezaAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 685 - 688

https://doi.org/10.1145/2370216.2370365

Published: 05 September 2012 Publication History

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Abstract

When performing eye detection in a driving scenario, new challenges arise that do not occur in a standard indoor eye tracking session. Rapid subject movement, non-controlled fast light variation and partial or total occlusions are the main problems that must be overcome. Furthermore, sunlight's infrared component makes it difficult the use of active artificial infrared light sources. In this paper, we describe a novel algorithm that combines Viola Jones face detector and TLD (Tracking Learning Detection) algorithm. In a standard driving scenario, it achieves a 84% rate of detection. Furthermore, we have designed a filtering stage that allows a low false positive rate. The algorithms hardware requirement is a standard web cam, and it can potentially work in real time.

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Z. Kalal, K. Mikolajczyk, and J. Matas. Face-TLD: Tracking-Learning-Detection Applied to Faces. International Conference on Image Processing, 2010.

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V. Ponz, A. Villanueva, L. Sesma, M. Ariz, and R. Cabeza. Topography-based detection of the iris centre using multiple-resolution images. In IMVIP 2011, pages 1--4, 2011.

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Cited By

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Edughele HZhang YMuhammad-Sukki FVien QMorris-Cafiero HOpoku Agyeman M(2022)Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral SclerosisIEEE Access10.1109/ACCESS.2022.316407510(41952-41972)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3164075
Klaib AAlsrehin NMelhem WBashtawi HMagableh A(2021)Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologiesExpert Systems with Applications10.1016/j.eswa.2020.114037166(114037)Online publication date: Mar-2021
https://doi.org/10.1016/j.eswa.2020.114037
Othman MAmaral TMcNaney RSmeddinck JVines JOlivier PJones MTscheligi MRogers YMurray-Smith R(2017)CrowdEyesProceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3098279.3098559(1-13)Online publication date: 4-Sep-2017
https://dl.acm.org/doi/10.1145/3098279.3098559
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Index Terms

Hybrid eye detection algorithm for outdoor environments
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2012

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Ubicomp '12

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Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing

September 5 - 8, 2012

Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

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Edughele HZhang YMuhammad-Sukki FVien QMorris-Cafiero HOpoku Agyeman M(2022)Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral SclerosisIEEE Access10.1109/ACCESS.2022.316407510(41952-41972)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3164075
Klaib AAlsrehin NMelhem WBashtawi HMagableh A(2021)Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologiesExpert Systems with Applications10.1016/j.eswa.2020.114037166(114037)Online publication date: Mar-2021
https://doi.org/10.1016/j.eswa.2020.114037
Othman MAmaral TMcNaney RSmeddinck JVines JOlivier PJones MTscheligi MRogers YMurray-Smith R(2017)CrowdEyesProceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3098279.3098559(1-13)Online publication date: 4-Sep-2017
https://dl.acm.org/doi/10.1145/3098279.3098559
Majaranta PBulling A(2014)Eye Tracking and Eye-Based Human–Computer InteractionAdvances in Physiological Computing10.1007/978-1-4471-6392-3_3(39-65)Online publication date: 27-Mar-2014
https://doi.org/10.1007/978-1-4471-6392-3_3

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