A Stochastic Nonlinear Dynamical System for Smoothing Noisy Eye Gaze Data
Journal:
arXiv
Published Date:
Apr 17, 2025
Abstract
In this study, we address the challenges associated with accurately
determining gaze location on a screen, which is often compromised by noise from
factors such as eye tracker limitations, calibration drift, ambient lighting
changes, and eye blinks. We propose the use of an extended Kalman filter (EKF)
to smooth the gaze data collected during eye-tracking experiments, and
systematically explore the interaction of different system parameters. Our
results demonstrate that the EKF significantly reduces noise, leading to a
marked improvement in tracking accuracy. Furthermore, we show that our proposed
stochastic nonlinear dynamical model aligns well with real experimental data
and holds promise for applications in related fields.
