Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.
翻译:社会机器人的快速发展刺激了在人类运动建模、解释和预测、主动避免碰撞、人类机器人相互作用和在共享空间共居方面进行积极研究。为此,现代方法需要高质量的培训和评估数据集。然而,大多数可用的数据集要么存在不准确的跟踪数据,要么存在被跟踪者不自然的脚本行为。本文件试图填补这一空白,为此提供了来自运动捕捉、眼睛-凝胶跟踪器和机载机器人传感器的高质量跟踪信息,在精密环境中提供这些信息。为了诱导记录参与者的自然行为,我们采用了松散的脚本任务任务任务,引导参与者以自然和有目的的方式通过动态实验室环境进行导航。本文提出的运动数据集设定了一个高质量的标准,因为现实和准确的数据通过语义信息得到加强,有利于开发新的算法,不仅依靠移动剂、静态和动态环境的跟踪信息,而且还依靠背景线索。