Social navigation and pedestrian behavior research has shifted towards machine learning-based methods and converged on the topic of modeling inter-pedestrian interactions and pedestrian-robot interactions. For this, large-scale datasets that contain rich information are needed. We describe a portable data collection system, coupled with a semi-autonomous labeling pipeline. As part of the pipeline, we designed a label correction web app that facilitates human verification of automated pedestrian tracking outcomes. Our system enables large-scale data collection in diverse environments and fast trajectory label production. Compared with existing pedestrian data collection methods, our system contains three components: a combination of top-down and ego-centric views, natural human behavior in the presence of a socially appropriate "robot", and human-verified labels grounded in the metric space. To the best of our knowledge, no prior data collection system has a combination of all three components. We further introduce our ever-expanding dataset from the ongoing data collection effort -- the TBD Pedestrian Dataset and show that our collected data is larger in scale, contains richer information when compared to prior datasets with human-verified labels, and supports new research opportunities.

翻译：社交导航与行人行为研究已转向基于机器学习的方法，并聚焦于行人交互及人-机器人交互建模这一主题。为此，需要包含丰富信息的大规模数据集。我们描述了一套便携式数据收集系统，并配备了半自动标注流程。作为该流程的一部分，我们设计了一个标签校正网络应用程序，以促进人工验证自动行人跟踪结果。我们的系统能够在多样化环境中实现大规模数据收集，并快速生成轨迹标签。与现有的行人数据收集方法相比，我们的系统包含三个组成部分：俯视视角与自我中心视角的结合、在社交适宜“机器人”存在下的自然人类行为，以及基于度量空间的人工验证标签。据我们所知，尚无先前的数据收集系统能同时具备这三个组成部分。此外，我们介绍了从持续数据收集工作中不断扩展的数据集——TBD行人数据集，并表明我们收集的数据比先前含有人工验证标签的数据集规模更大、信息更丰富，且支持新的研究机会。