用扩展卡尔曼滤波提升眼动追踪数据精度
A Stochastic Nonlinear Dynamical System for Smoothing Noisy Eye Gaze Data
- 构建随机非线性动力学模型，用EKF滤除眼动噪声
- 显著降低数据噪声，提升追踪准确率
- 适合需要高精度眼动分析的研究者使用
本研究针对屏幕注视位置识别受眼动仪性能限制、校准漂移、环境光照变化及眨眼等因素导致的噪声问题，提出采用扩展卡尔曼滤波（EKF）对眼动实验中的原始数据进行平滑处理，并系统研究了不同系统参数的交互影响。结果表明，EKF能有效降低噪声，显著提升追踪准确性。此外，所提出的随机非线性动力学模型与真实实验数据高度吻合，具有在相关领域应用的潜力。
原文摘要 · Abstract (English)
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.
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