Recently, pedestrian behavior research has shifted towards machine learning based methods and converged on the topic of modeling pedestrian interactions. For this, a large-scale dataset that contains rich information is needed. We propose a data collection system that is portable, which facilitates accessible large-scale data collection in diverse environments. We also couple the system with a semi-autonomous labeling pipeline for fast trajectory label production. We further introduce the first batch of dataset from the ongoing data collection effort -- the TBD pedestrian dataset. Compared with existing pedestrian datasets, our dataset contains three components: human verified labels grounded in the metric space, a combination of top-down and perspective views, and naturalistic human behavior in the presence of a socially appropriate "robot".

翻译：近期，行人行为研究已转向基于机器学习的方法，并聚焦于行人交互建模这一课题。为此，需要包含丰富信息的大规模数据集。我们提出了一套可移植的数据收集系统，该系统的便携性有助于在多样环境中实现可访问的大规模数据收集。同时，我们将该系统与半自动标注流程相结合，以快速生成轨迹标签。我们进一步介绍了正在进行的收集工作中的首批数据集——TBD 行人数据集。与现有行人数据集相比，该数据集包含三个组成部分：基于度量空间的人工验证标签、顶部视角与透视视角的结合，以及在存在社交适当的“机器人”时的自然行人行为。