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Slippage-robust linear features for eye tracking
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AI
DOI：10.1016/j.eswa.2024.125799.png)
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Abstract
En 中文
Regression-based gaze estimation for wearable eye-tracking headsets inevitably suffers from headset slippage. Despite good calibration accuracy (< 2 degrees), over time, slippage can cause the gaze estimation accuracy to deteriorate significantly, ranging from 2 degrees to 40 degrees. Existing corrective measures typically cater to large viewing depths (> 1 meter). However, these measures are unsuitable for lower depth, such as industrial or office settings, where objects of interest lie within arm's reach. For a dataset with natural slippage, we propose a set of slippage-robust features along with a linear regression function to improve gaze estimation accuracy. We also propose metrics that are more informative about gaze estimation accuracy over the entire field of view. Our findings demonstrate that the proposed features improve the gaze estimation accuracy by nearly 30%. These contributions pave the way for improved performance and reliability of eye-tracking headsets, enabling their use in diverse research domains and real-world applications.
Keywords:
Calibration
Drift
Eye tracking
Gaze estimation
Gaze tracking
Head-mounted display
Pupil core
Slippage
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
