研究移动中头部动作如何影响手机屏幕注视估计精度。
Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions
- 通过真实场景实验分析用户移动时的注视误差来源。
- 动态条件下误差最高达静态的1.49倍（48.91%增长）。
- 适合开发自适应眼动追踪系统的研究者参考。
手机注视追踪旨在通过设备前置摄像头拍摄的面部图像推断用户在屏幕上的注视点或方向。尽管该技术推动了众多注视交互应用的发展，但在移动场景中，由于用户与设备间空间关系动态变化及行为多样性，保持稳定精度仍具挑战。本文通过两项用户研究，在从平躺到迷宫导航等多种运动状态和交互任务下收集行为与注视数据，量化分析发现日常任务存在行为规律，头距离、头姿态和设备朝向是影响精度的关键因素。相较于静态条件，动态环境下误差最高上升48.91%。结果表明，亟需更鲁棒、可自适应的注视追踪系统，以应对头部运动和设备偏转，确保在多样移动场景下的准确表现。
原文摘要 · Abstract (English)
Mobile gaze tracking involves inferring a user's gaze point or direction on a mobile device's screen from facial images captured by the device's front camera. While this technology inspires an increasing number of gaze-interaction applications, achieving consistent accuracy remains challenging due to dynamic user-device spatial relationships and varied motion conditions inherent in mobile contexts. This paper provides empirical evidence on how user mobility and behaviour affect mobile gaze tracking accuracy. We conduct two user studies collecting behaviour and gaze data under various motion conditions - from lying to maze navigation - and during different interaction tasks. Quantitative analysis has revealed behavioural regularities among daily tasks and identified head distance, head pose, and device orientation as key factors affecting accuracy, with errors increasing by up to 48.91% in dynamic conditions compared to static ones. These findings highlight the need for more robust, adaptive eye-tracking systems that account for head movements and device deflection to maintain accuracy across diverse mobile contexts.
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