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Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions
arXiv:2502.10570v3 Announce Type: replace-cross Abstract: Mobile gaze tracking on handheld devices commonly relies on appearance-based 2D gaze estimation from the device's front-facing RGB camera. Alt
arXiv:2502.10570v3 Announce Type: replace-cross Abstract: Mobile gaze tracking on handheld devices commonly relies on appearance-based 2D gaze estimation from the device's front-facing RGB camera. Although this approach makes eye tracking more accessible by avoiding dedicated infrared hardware, maintaining accuracy remains difficult because the spatial relationship among the user's face, the camera, and the screen changes continuously during handheld use. This paper examines how motion-related geometry change affects a representative front-camera RGB 2D gaze-estimation pipeline. We report two complementary within-subject user studies ((n=10)) that capture handheld behaviour, calibration data, and gaze-estimation error during everyday-style mobile tasks across conditions ranging from lying and sitting to walking and maze navigation. Rather than treating motion as a single binary factor, we analyse within-task versus task-switch behaviour, calibration-test mismatch, and condition-specific motion signatures. Behavioural results show that head-to-camera distance is comparatively stable during sustained within-task use, while task switches introduce much larger geometry fluctuations, with head-distance standard deviations of approximately 7.42--10.10,cm across conditions. In the gaze-evaluation study, one-off calibration reduced average RMSE from 4.49,cm to 3.44,cm; under this regime, the global effect of motion condition on RMSE was not statistically reliable, whereas clearer patterns appeared in task-specific and motion-specific calibration analyses, where better calibration-test match reduced error.
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Source: arXiv cs.CV | 2026-10-15