eyeNotate: Interactive Annotation of Mobile Eye Tracking Data Based on Few-Shot Image Classification
Michael Barz, Omair Shahzad Bhatti, Hasan Md Tusfiqur Alam, Duy Minh Ho Nguyen, Kristin Altmeyer, Sarah Malone, Daniel Sonntag
TL;DR
eyeNotate is a web-based tool that helps annotate mobile eye tracking data more efficiently using interactive and semi-automatic methods.
Contribution
The tool introduces a few-shot image classification model to improve annotation efficiency and usability.
Findings
The IML-support version improved annotation efficiency compared to the baseline.
Expert annotators found the tool usable and reliable for mapping fixations to AOIs.
Three image classification models were tested for performance on remaining data.
Abstract
Mobile eye tracking is an important tool in psychology and human-centered interaction design for understanding how people process visual scenes and user interfaces. However, analyzing recordings from head-mounted eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we develop eyeNotate, a web-based annotation tool that enables semi-automatic data annotation and learns to improve from corrective user feedback. Users can manually map fixation events to areas of interest (AOIs) in a video-editing-style interface (baseline version). Further, our tool can generate fixation-to-AOI mapping suggestions based on a few-shot image classification model (IML-support version). We conduct an expert study with trained annotators (n = 3) to compare the baseline and IML-support versions. We…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · Retinal Imaging and Analysis · Visual Attention and Saliency Detection