How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis

TL;DR

This paper introduces depth-adaptive thumbnails that vary in size based on eye-to-object distance, enhancing image-based gaze analysis by emphasizing focus areas in augmented reality settings.

Contribution

It proposes a novel method for adjusting thumbnail size according to distance, improving the accuracy of gaze data analysis and visualization.

Findings

Depth-adaptive thumbnails improve scanpath similarity measures.

Considering distance enhances visualization of gaze focus.

Method benefits augmented reality gaze analysis.

Abstract

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.