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Social navigation and pedestrian behavior research has shifted towardsmachine learning-based methods and converged on the topic of modelinginter-pedestrian interactions and pedestrian-robot interactions. For this,large-scale datasets that contain rich information are needed. We describe aportable data collection system, coupled with a semi-autonomous labelingpipeline. As part of the pipeline, we designed a label correction web app thatfacilitates human verification of automated pedestrian tracking outcomes. Oursystem enables large-scale data collection in diverse environments and fasttrajectory label production. Compared with existing pedestrian data collectionmethods, our system contains three components: a combination of top-down andego-centric views, natural human behavior in the presence of a sociallyappropriate "robot", and human-verified labels grounded in the metric space. Tothe best of our knowledge, no prior data collection system has a combination ofall three components. We further introduce our ever-expanding dataset from theongoing data collection effort -- the TBD Pedestrian Dataset and show that ourcollected data is larger in scale, contains richer information when compared toprior datasets with human-verified labels, and supports new researchopportunities.

TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Data