1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning

Wolfgang Fuhl, Johannes Schneider, Enkelejda Kasneci

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

5 Zitate (Scopus)

Abstract

In this paper we present a new approach for pupil segmentation. It can be computed and trained very efficiently, making it ideal for online use for high speed eye trackers as well as for energy saving pupil detection in mobile eye tracking. The approach is inspired by the BORE and CBF algorithms and generalizes the binary comparison by Haar features. Since these features are intrinsically very susceptible to noise and fluctuating light conditions, we combine them with conditional pupil shape probabilities. In addition, we also rank each feature according to its importance in determining the pupil shape. Another advantage of our method is the use of statistical learning, which is very efficient and can even be used online. https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FStatsPupilmode=list.

OriginalspracheEnglisch
TitelProceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten3459-3469
Seitenumfang11
ISBN (elektronisch)9781665401913
DOIs
PublikationsstatusVeröffentlicht - 2021
Extern publiziertJa
Veranstaltung18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Kanada
Dauer: 11 Okt. 202117 Okt. 2021

Publikationsreihe

NameProceedings of the IEEE International Conference on Computer Vision
Band2021-October
ISSN (Print)1550-5499

Konferenz

Konferenz18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Land/GebietKanada
OrtVirtual, Online
Zeitraum11/10/2117/10/21

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