A Stochastic Nonlinear Dynamical System for Smoothing Noisy Eye Gaze Data
This work addresses noise issues in eye-tracking for applications like human-computer interaction, but it is incremental as it applies an existing method to a specific domain.
The study tackled the problem of noisy eye gaze data from eye trackers by using an extended Kalman filter for smoothing, resulting in significant noise reduction and improved tracking accuracy.
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.