论文标题
迈向富人，便携式和大规模的行人数据收集
Towards Rich, Portable, and Large-Scale Pedestrian Data Collection
论文作者
论文摘要
最近，行人行为研究已转向基于机器学习的方法，并融合了对行人相互作用进行建模的主题。为此，需要一个包含丰富信息的大规模数据集。我们提出了一个可移植的数据收集系统，该系统有助于在不同环境中访问的大规模数据收集。我们还将该系统与半自治的标签管道搭配使用，用于快速轨迹标签的产生。我们进一步介绍了正在进行的数据收集工作 - TBD行人数据集的第一批数据集。与现有的行人数据集相比，我们的数据集包含三个组件：建立在公制空间中的人类验证标签，自上而下和透视视图的结合以及自然主义的人类行为，在存在社会上适合的“机器人”的情况下。
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".
