发表机构
Chung-Ang University; Electronics and Telecommunications Research Institute（中央大学; 电子电信研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究引入GazeDepth眼动追踪数据集，包含19名参与者在真实场景任务中收集的多类数据，可区分近、中、远三种观看距离，支持真实场景下的注视距离推断与感知距离交互。
AI 中文摘要
从注视行为估计观看距离对于理解用户意图并实现感知距离的交互系统至关重要。然而，大多数现有的眼动追踪数据集是在受限环境中收集的，例如实验室环境或静态任务，因此它们仅部分捕捉了观看距离随头部和身体自然移动而变化的真实场景中的观看行为。我们引入GazeDepth，这是一个由19名参与者使用可穿戴追踪器在反映真实场景的任务中收集的眼动追踪数据集。GazeDepth包含固定距离观看场景，观察者与目标的距离固定为近（33厘米）、中（50厘米）和远（300厘米），以及可变距离观看场景，参与者在室内和室外环境中不同深度的目标之间转移注视。该数据集提供同步的注视数据、瞳孔大小、3D眼向量和头部运动信号，以及距离标签。统计分析显示，与距离相关的注视特征（如辐辏角和估计的观看距离）在不同观看距离类别中存在一致差异。此外，在GazeDepth上训练的分类模型进一步证明，该数据集捕捉了能够区分三种观看距离类别的注视特征，支持在真实场景中基于注视的距离推断和感知距离的交互。
英文摘要
Estimating viewing distance from gaze behavior is essential for understanding user intent and enabling distance-aware interactive systems. However, most existing eye-tracking datasets have been collected in constrained settings, such as laboratory environments or static tasks. Consequently, they only partially capture viewing behaviors in real-world situations where viewing distance changes with natural head and body movements. We introduce GazeDepth, an eye-tracking dataset collected from 19 participants using a wearable tracker during tasks reflecting real-world scenarios. GazeDepth includes fixed-distance viewing scenarios with constant observer-target distances at near (33 cm), middle (50 cm), and far (300 cm), as well as variable-distance viewing scenarios in which participants shift gaze among targets at different depths in indoor and outdoor environments. The dataset provides synchronized gaze data, pupil size, 3D eye-vectors, and head-motion signals, along with distance labels. Statistical analyses showed that distance-related gaze features, such as vergence angle and estimated viewing distance, differed consistently across viewing-distance categories. In addition, classification models trained on GazeDepth further demonstrated that the dataset captures gaze characteristics that distinguish the three viewing-distance categories, supporting gaze-based distance inference and distance-aware interaction in realistic scenarios.
Comments22 pages, 6 figures, Manuscript submitted to Scientific Data
