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Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching

In this short article we present our manual annotation of the eye movement events in a subset of the large-scale eye tracking data set Hollywood2. Our labels include fixations, saccades, and smooth pursuits, as well as a noise event type (the latter representing either blinks, loss of tracking, or p...

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Detalles Bibliográficos
Autores principales: Agtzidis, Ioannis, Startsev, Mikhail, Dorr, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bern Open Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8005322/
https://www.ncbi.nlm.nih.gov/pubmed/33828806
http://dx.doi.org/10.16910/jemr.13.4.5
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author Agtzidis, Ioannis
Startsev, Mikhail
Dorr, Michael
author_facet Agtzidis, Ioannis
Startsev, Mikhail
Dorr, Michael
author_sort Agtzidis, Ioannis
collection PubMed
description In this short article we present our manual annotation of the eye movement events in a subset of the large-scale eye tracking data set Hollywood2. Our labels include fixations, saccades, and smooth pursuits, as well as a noise event type (the latter representing either blinks, loss of tracking, or physically implausible signals). In order to achieve more consistent annotations, the gaze samples were labelled by a novice rater based on rudimentary algorithmic suggestions, and subsequently corrected by an expert rater. Overall, we annotated eye movement events in the recordings corresponding to 50 randomly selected test set clips and 6 training set clips from Hollywood2, which were viewed by 16 observers and amount to a total of approximately 130 minutes of gaze data. In these labels, 62.4% of the samples were attributed to fixations, 9.1% – to saccades, and, notably, 24.2% – to pursuit (the remainder marked as noise). After evaluation of 15 published eye movement classification algorithms on our newly collected annotated data set, we found that the most recent algorithms perform very well on average, and even reach human-level labelling quality for fixations and saccades, but all have a much larger room for improvement when it comes to smooth pursuit classification. The data set is made available at https://gin.g-node.org/ioannis.agtzidis/hollywood2_em.
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spelling pubmed-80053222021-04-06 Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching Agtzidis, Ioannis Startsev, Mikhail Dorr, Michael J Eye Mov Res Research Article In this short article we present our manual annotation of the eye movement events in a subset of the large-scale eye tracking data set Hollywood2. Our labels include fixations, saccades, and smooth pursuits, as well as a noise event type (the latter representing either blinks, loss of tracking, or physically implausible signals). In order to achieve more consistent annotations, the gaze samples were labelled by a novice rater based on rudimentary algorithmic suggestions, and subsequently corrected by an expert rater. Overall, we annotated eye movement events in the recordings corresponding to 50 randomly selected test set clips and 6 training set clips from Hollywood2, which were viewed by 16 observers and amount to a total of approximately 130 minutes of gaze data. In these labels, 62.4% of the samples were attributed to fixations, 9.1% – to saccades, and, notably, 24.2% – to pursuit (the remainder marked as noise). After evaluation of 15 published eye movement classification algorithms on our newly collected annotated data set, we found that the most recent algorithms perform very well on average, and even reach human-level labelling quality for fixations and saccades, but all have a much larger room for improvement when it comes to smooth pursuit classification. The data set is made available at https://gin.g-node.org/ioannis.agtzidis/hollywood2_em. Bern Open Publishing 2020-07-27 /pmc/articles/PMC8005322/ /pubmed/33828806 http://dx.doi.org/10.16910/jemr.13.4.5 Text en This work is licensed under a Creative Commons Attribution 4.0 International License, ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Research Article
Agtzidis, Ioannis
Startsev, Mikhail
Dorr, Michael
Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching
title Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching
title_full Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching
title_fullStr Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching
title_full_unstemmed Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching
title_short Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching
title_sort two hours in hollywood: a manually annotated ground truth data set of eye movements during movie clip watching
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8005322/
https://www.ncbi.nlm.nih.gov/pubmed/33828806
http://dx.doi.org/10.16910/jemr.13.4.5
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