实时精准识别阅读时的视线行归属，提升眼动追踪交互体验。
Sure About That Line? Approaching Confidence-Based, Real-Time Line Assignment in Reading Gaze Data
- 基于阅读行为与高斯线性似然融合计算置信度得分
- 每注视延迟仅0.348毫秒，线上离线误差差仅1-2%
- 对儿童阅读数据准确率超95%，抗回视能力强
远程和摄像头眼动追踪在多行阅读中受多种噪声和版面模糊影响，而实时阅读辅助亟需可靠的逐注视行分配。以往方法多为事后处理或限制行为（如禁止回读），削弱了交互性。本文提出CONF-LA（基于置信度的在线注视-行分配），结合阅读行为知识与注视点上的高斯线性似然，计算后验行得分，并在不确定性高时推迟分配。在现有开源数据集上评估显示，其在事后分析中表现稳定，线上与离线性能差距缩小至1-2%，平均每注视延迟仅为0.348毫秒。该方法对回视具有显著鲁棒性，在儿童数据上实现约95%的即兴中位准确率，优于所有测试算法。本文鼓励在此方向进一步研究，并讨论未来拓展可能。
原文摘要 · Abstract (English)
Remote and webcam-based eye tracking in multi-line reading suffers from various noise factors and layout ambiguity, precisely where real-time reading support needs reliable, per-fixation line assignment. Prior work largely addresses this challenge post hoc or by restricting behavior (e.g., disallowing re-reading), undermining interactive use. We propose CONF-LA (Confidence-score-based Online Fixation-to-Line Assignment), a principled, low-latency approach that integrates knowledge about reading behavior and Gaussian line likelihoods over fixations to compute a posterior-line-score and defers assignments when uncertainty is high. Evaluated on existing open-source data, CONF-LA demonstrates stable performance in post hoc analysis and closes the online-offline gap (1-2 %) with a mean per-fixation latency of 0.348 ms. Our approach exhibits particular invariance toward regressions, yielding significant improvement in ad hoc median accuracies on children data (approx. 95 %) over all tested algorithms. We encourage further research in this direction and discuss possibilities for future development.
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