Towards Rich, Portable, and Large-Scale Pedestrian Data Collection
This provides a scalable solution for pedestrian interaction research, though it is incremental as it builds on existing data collection methods.
The authors tackled the need for large-scale, rich pedestrian data by developing a portable collection system and semi-autonomous labeling pipeline, resulting in the TBD dataset with verified labels, multiple views, and naturalistic behavior.
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".