Drift Calibration in Geometric Eye Tracking Systems

2026-08-30

AI Summary

研究解决了几何眼动追踪系统中的校准误差问题，通过创建标准化数据集并评估多种校准方法，包括引入轻量级神经优化器，有效降低了校准误差。

Abstract

Geometric eye trackers can provide the spatial accuracy required for gaze-based interaction and multimodal studies, but their measurements remain sensitive to residual session-specific calibration error. Research on correcting this error is difficult to compare because methods are typically evaluated with different devices, target layouts, and error definitions. We present a calibration-focused dataset containing 163 trials from 12 participants, with separate 18-point fitting and 32-point test grids, and use it to evaluate global, local, and composite correction functions under a common spatial-extrapolation protocol. We further introduce a lightweight neural refiner that combines ranked predictions from complementary calibrators. On this controlled dataset, post-vendor correction reduces the mean angular error from $1.53^\circ$ to $1.03^\circ$ with the strongest classical composite and to $0.96^\circ$ with the refiner. In a closed-loop gaze task, lower residual error is associated with higher performance across four online correction conditions. These results provide a reproducible data-quality benchmark for using gaze as a behavioral signal in interactive modeling.

Problem

• geometric eye trackers
• calibration error
• gaze-based interaction

Innovation

• Calibration Dataset
• Neural Refiner
• Spatial-Extrapolation Protocol