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SacCalib: Reducing Calibration Distortion for Stationary Eye Trackers Using Saccadic Eye Movements

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Huang,  Michael Xuelin
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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arXiv:1903.04047.pdf
(プレプリント), 2MB

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引用

Huang, M. X., & Bulling, A. (2019). SacCalib: Reducing Calibration Distortion for Stationary Eye Trackers Using Saccadic Eye Movements. In K., Krejtz, & B., Sharif (Eds.), Proceedings ETRA 2019. New York, NY: ACM. doi:10.1145/3317956.3321553.


引用: https://hdl.handle.net/21.11116/0000-0003-2BF3-B
要旨
Recent methods to automatically calibrate stationary eye trackers were shown
to effectively reduce inherent calibration distortion. However, these methods
require additional information, such as mouse clicks or on-screen content. We
propose the first method that only requires users' eye movements to reduce
calibration distortion in the background while users naturally look at an
interface. Our method exploits that calibration distortion makes straight
saccade trajectories appear curved between the saccadic start and end points.
We show that this curving effect is systematic and the result of distorted gaze
projection plane. To mitigate calibration distortion, our method undistorts
this plane by straightening saccade trajectories using image warping. We show
that this approach improves over the common six-point calibration and is
promising for reducing distortion. As such, it provides a non-intrusive
solution to alleviating accuracy decrease of eye tracker during long-term use.