Abstract
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.
Highlights
In recent years eye tracking has gained further popularity in various fields, and has been applied in increasingly unconstrained scenarios, both in research and commercial applications
To better understand the characteristics of the three labelled eye movement types in this data set, we present in Figure 3 the distributions of their speeds, durations, and amplitudes
The amplitude was computed as the distance between the first and the last samples in each eye movement interval, while the speed was computed by dividing the amplitude by the respective interval duration
Summary
In recent years eye tracking has gained further popularity in various fields, and has been applied in increasingly unconstrained scenarios, both in research and commercial applications. These new fields of application move away from stimuli that use clearly defined targets on a monitor and towards more naturalistic content and environments (e.g. movies, virtual reality, everyday life). Received December 20, 2019; Published December 27, 2020. Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.