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To study an observer's eye movements during realistic tasks, the observer should be free to move naturally throughout our three-dimensional world. Therefore, a technique to determine an observer's <i>point-of-regard (POR)</i> as well as his/her motion throughout a scene in three dimensions with minor user input is proposed. This requires robust feature(More)
Video-based eye trackers produce an output video showing where a subject is looking, the subject's Point-of-Regard (POR), for each frame of a video of the scene. This information can be extremely valuable, but its analysis can be overwhelming. Analysis of eye-tracked data from portable (wearable) eye trackers is especially daunting, as the scene video may(More)
Video-based eye trackers produce an output video showing where a subject is looking, the subject's <i>point-of-regard (POR)</i>, for each frame of a video of the scene. Fixation-identification algorithms simplify the long list of POR data into a more manageable set of data, especially for further analysis, by grouping PORs into fixations. Most current(More)
Our portable video-based monocular eye tracker contains a headgear with two cameras that capture videos of the observer's right eye and the scene from the observer's perspective (Figure 1a). With this eye tracker, we typically obtain a position -- that represents the observer's <i>point of regard (POR)</i> -- in each frame of the scene video (Figure 1b(More)
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