How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis
arxiv(2024)
摘要
Image thumbnails are a valuable data source for fixation filtering, scanpath
classification, and visualization of eye tracking data. They are typically
extracted with a constant size, approximating the foveated area. As a
consequence, the focused area of interest in the scene becomes less prominent
in the thumbnail with increasing distance, affecting image-based analysis
techniques. In this work, we propose depth-adaptive thumbnails, a method for
varying image size according to the eye-to-object distance. Adjusting the
visual angle relative to the distance leads to a zoom effect on the focused
area. We evaluate our approach on recordings in augmented reality,
investigating the similarity of thumbnails and scanpaths. Our quantitative
findings suggest that considering the eye-to-object distance improves the
quality of data analysis and visualization. We demonstrate the utility of
depth-adaptive thumbnails for applications in scanpath comparison and
visualization.
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