发表机构
University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对长时间窄范围屏幕注视场景，通过基准测试提出的差分注视模型等方法，证实差分估计可提升注视估计性能，为该场景下的可靠眼动追踪提供了有前景的基础。
AI 中文摘要
基于外观的注视估计为屏幕相关的行为和临床应用提供了一种低成本的红外眼动追踪替代方案，然而现有模型通常是为大范围的注视角度和头部姿态开发的。长时间屏幕注视是一种独特的场景，其中注视始终靠近屏幕中心，头部运动受限，且校准漂移会随时间累积。在本研究中，我们在140段带有同步眼动追踪的长时间面部视频记录上，对6种已发表的估计器以及我们提出的差分注视模型进行基准测试，采用受试者不相交评估方案和统一的校准帧预算。差分估计是唯一一种显著优于各记录平均注视基线预测器的静态方法，且通过添加时间上下文进一步提升了性能。它还与参考注视点和扫视运动达成了最强的一致性，这表明仅低角度误差不足以验证眼动重建的有效性。这些发现确立了差分估计作为长时间、窄范围场景下可靠眼动追踪的有前景基础。
英文摘要
Appearance-based gaze estimation offers a low-cost alternative to infrared eye tracking for screen-based behavioral and clinical applications, however existing models are typically developed for wide ranges of gaze angle and head pose. Prolonged screen viewing presents a distinct regime in which gaze remains near the screen center, head motion is limited, and calibration drift accumulates over time. In this work, we benchmark six published estimators along with a proposed differential-gaze model on 140 long-duration facial video recordings with synchronized eye tracking under subject-disjoint evaluation and a common budget of calibration frames. Differential estimation was the only static method that significantly improved upon a baseline predictor of each recording's mean gaze, and was further boosted by addition of temporal context. It also yielded the strongest agreement with reference fixations and saccades, demonstrating that low angular error alone could not validate eye movement reconstruction. These findings establish differential estimation as a promising foundation for reliable gaze tracking in long-duration, narrow-range settings.
