Towards Rich, Portable, and Large-Scale Pedestrian Data Collection

• URL: http://arxiv.org/abs/2203.01974v2
• Date: Fri, 29 Sep 2023 12:29:29 GMT
• Title: Towards Rich, Portable, and Large-Scale Pedestrian Data Collection
• Authors: Allan Wang, Abhijat Biswas, Henny Admoni, Aaron Steinfeld
• Abstract summary: We propose a data collection system that is portable, which facilitates accessible large-scale data collection in diverse environments. We 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"
• Score: 6.250018240133604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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".

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