Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present TH\"OR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. TH\"OR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies.
翻译:理解人类行为是机器人和智能系统与人共享空间的关键。 因此, 使这些系统能够感知、 跟踪、 学习和预测人类行为, 以及与人类规划和互动的研究在过去几年中受到越来越多的关注。 包含相关困难程度的大型人类运动数据集的可用性对于这项研究来说至关重要。 现有的数据集在信息内容、 注释质量或人类行为变异方面往往有限。 在本文中, 我们提供了人类运动轨迹和在室内环境中收集的目视数据的新数据集, 其位置、 方向、 视觉方向、 社会组合、 障碍地图和目标坐标等准确的地面事实。 TH\ OR 还包含由 3D lidar 收集的传感器数据, 并包含移动机器人对空间的导航。 我们提出了一组计量指标, 用于对运动轨迹数据集进行定量分析, 如平均跟踪时间、 地面真相噪音、 曲调和速度变化。 与前艺术相比, 我们的数据集在人类运动行为方面有较大种类, 并且含有更高频率的描述。