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".
@article{arxiv.2203.01974,
title = {Towards Rich, Portable, and Large-Scale Pedestrian Data Collection},
author = {Allan Wang and Abhijat Biswas and Henny Admoni and Aaron Steinfeld},
journal= {arXiv preprint arXiv:2203.01974},
year = {2023}
}
Comments
IROS 2022 Workshop paper (Evaluating Motion Planning Performance: Metrics, Tools, Datasets, and Experimental Design)