Appearance-based gaze estimation systems have shown great progress recently, yet the performance of these techniques depend on the datasets used for training. Most of the existing gaze estimation datasets setup in interactive settings were recorded in laboratory conditions and those recorded in the wild conditions display limited head pose and illumination variations. Further, we observed little attention so far towards precision evaluations of existing gaze estimation approaches. In this work, we present a large gaze estimation dataset, PARKS-Gaze, with wider head pose and illumination variation and with multiple samples for a single Point of Gaze (PoG). The dataset contains 974 minutes of data from 28 participants with a head pose range of 60 degrees in both yaw and pitch directions. Our within-dataset and cross-dataset evaluations and precision evaluations indicate that the proposed dataset is more challenging and enable models to generalize on unseen participants better than the existing in-the-wild datasets. The project page can be accessed here: https://github.com/lrdmurthy/PARKS-Gaze

翻译：基于外貌的视线估计系统近年来取得了显著进展，但这些技术的性能依赖于训练所用的数据集。现有大多数交互场景下的视线估计数据集在实验室条件下采集，而野外场景下采集的数据集则存在头部姿态和光照变化有限的问题。此外，我们注意到目前对现有视线估计方法的精度评估关注甚少。本文提出一个大规模视线估计数据集PARKS-Gaze，该数据集具有更广泛的头部姿态和光照变化，并为单个注视点（PoG）提供多个样本。数据集包含28名参与者共计974分钟的数据，头部姿态在偏航和俯仰方向上的范围均达60度。我们的数据集内评估、跨数据集评估以及精度评估表明，所提数据集更具挑战性，且能使模型在未见参与者上的泛化能力优于现有野外数据集。项目页面可通过以下链接访问：https://github.com/lrdmurthy/PARKS-Gaze