How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914774504177664 |
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| author | Koch, Maurice Pathmanathan, Nelusa Weiskopf, Daniel Kurzhals, Kuno |
| author_facet | Koch, Maurice Pathmanathan, Nelusa Weiskopf, Daniel Kurzhals, Kuno |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_18680 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis Koch, Maurice Pathmanathan, Nelusa Weiskopf, Daniel Kurzhals, Kuno Human-Computer Interaction 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. |
| title | How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.18680 |