用弱监督学习提升视网膜眼动追踪精度与鲁棒性
Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework
- 基于弱监督学习框架，替代传统模板匹配方法
- 6人实验中95%分位眼动误差小于0.45度
- 适合眼科成像与高精度AR/VR眼动系统研究者
基于视网膜图像的眼动追踪广泛应用于眼科成像与视觉科学，是实现比当前主流AR/VR设备中基于瞳孔和角膜的方法更高注视精度的有前景路径。然而，现有视网膜追踪算法仍主要依赖经典模板匹配配准，对视网膜特征变化及真实成像条件的鲁棒性不足。本文提出一种新型弱监督、基于学习的视网膜眼动追踪框架。初步研究表明，该方法具有高精度，在6名受试者组成的群体中，95%分位眼动误差低于0.45度。
原文摘要 · Abstract (English)
Retinal image-based eye tracking is widely used in ophthalmic imaging and vision science, and is a promising path to deliver higher gaze accuracy than the pupil- and cornea-based approaches commonly used in modern AR/VR devices. Nevertheless, existing retinal tracking algorithms still primarily rely on classical template-matching registration, which can be insufficiently robust to retinal feature variability and real-world imaging conditions. In this work, we propose a novel weakly-supervised, learning-based framework for robust retinal eye tracking. Initial studies demonstrate high accuracy, achieving the 95th-percentile gaze error < 0.45 deg across a cohort of 6 participants.
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