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：基于外观的视线估计系统在近期取得了显著进展，但这些技术的性能高度依赖于训练所用数据集。现有的大多数交互式视线估计数据集均采集于实验室环境，而野外环境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，我们注意到现有视线估计方法在精度评估方面鲜有关注。本研究提出了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，并对单个注视点（PoG）采集了多样本。该数据集包含28名参与者的974分钟数据，头部姿态范围在偏航和俯仰方向上均达到60度。我们的数据集内评估、跨数据集评估及精度评估结果表明，相较于现有野外数据集，本数据集更具挑战性，且能使模型在未见参与者上实现更优的泛化性能。项目页面访问地址：https://github.com/lrdmurthy/PARKS-Gaze