English(EN) Drift Calibration in Geometric Eye Tracking Systems

新型神经精炼器提升几何眼动追踪器精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新方法来提高几何眼动追踪器的准确性，这对于基于注视的交互和多模态研究至关重要。所提出的技术涉及一种轻量级的神经精炼器，它结合了多个校准器的预测，以减少特定会话的校准误差。在受控数据集上进行测试时，与经典方法相比，该方法显著降低了平均角度误差，证明了其在增强交互式建模中注视作为行为信号的可靠性方面的潜力。 AI

影响通过提高眼动追踪精度，增强了基于注视的交互和多模态研究的可靠性。

排序理由该条目是发表在arXiv上的研究论文，详细介绍了一种提高眼动追踪精度的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jiaqi Liu, Zixuan Wang, Yuhong Zhang, Dingkang Liang, Jane Hanqi Li, Tzyy-Ping Jung, Gert Cauwenberghs · 2026-09-01 04:00

几何眼动追踪系统中的漂移校准

arXiv:2608.29739v1 Announce Type: new 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 …

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报道来源 [1]

几何眼动追踪系统中的漂移校准

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