  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.
