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Feature matching and ArUco markers application in mobile eye tracking studies

Authors: Adam Bykowski; Szymon Kupinski;

Feature matching and ArUco markers application in mobile eye tracking studies

Abstract

This paper presents eye tracking glasses data analysis automation techniques, utilizing image processing. Two separate techniques will be described. One method is used to automate mapping of point-of-regard to a static reference image using a feature matching algorithm AKAZE. The second method utilizes ArUco markers for mapping of point-of-regard to a screencast from a mobile device. The described methods are used to aggregate experiment statistical data for future analysis and presentation in forms like heatmaps or gaze plots. Algorithms are implemented in Python 3.6 and OpenCV library.

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Average
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