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  Computational Modelling and Prediction of Gaze Estimation Error for Head-mounted Eye Trackers

Barz, M., Bulling, A., & Daiber, F.(2015). Computational Modelling and Prediction of Gaze Estimation Error for Head-mounted Eye Trackers (15-01). Saarbrücken: DFKI.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-B972-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0025-09CA-9
Genre: Report

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 Creators:
Barz, Michael1, Author
Bulling, Andreas2, Author           
Daiber, Florian1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              

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 Abstract: Head-mounted eye tracking has significant potential for mobile gaze-based interaction with ambient displays but current interfaces lack information about the tracker\'s gaze estimation error. Consequently, current interfaces do not exploit the full potential of gaze input as the inherent estimation error can not be dealt with. The error depends on the physical properties of the display and constantly varies with changes in position and distance of the user to the display. In this work we present a computational model of gaze estimation error for head-mounted eye trackers. Our model covers the full processing pipeline for mobile gaze estimation, namely mapping of pupil positions to scene camera coordinates, marker-based display detection, and display mapping. We build the model based on a series of controlled measurements of a sample state-of-the-art monocular head-mounted eye tracker. Results show that our model can predict gaze estimation error with a root mean squared error of 17.99~px ($1.96^\\circ$).

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Language(s): eng - English
 Dates: 2015-01-01
 Publication Status: Published online
 Pages: 10 p.
 Publishing info: Saarbrücken : DFKI
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Barz_Rep15
Report Nr.: 15-01
URN: https://perceptual.mpi-inf.mpg.de/files/2015/01/gazequality.pdf
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Title: DFKI Research Report
  Abbreviation : RR
Source Genre: Series
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