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

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.

Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective

Blind people have limited access to information about their surroundings, which is important for ensuring one's safety, managing social interactions, and identifying approaching pedestrians. With advances in computer vision, wearable cameras can provide equitable access to such information. However, the always-on nature of these assistive technologies poses privacy concerns for parties that may get recorded. We explore this tension from both perspectives, those of sighted passersby and blind users, taking into account camera visibility, in-person versus remote experience, and extracted visual information. We conduct two studies: an online survey with MTurkers (N=206) and an in-person experience study between pairs of blind (N=10) and sighted (N=40) participants, where blind participants wear a working prototype for pedestrian detection and pass by sighted participants. Our results suggest that both of the perspectives of users and bystanders and the several factors mentioned above need to be carefully considered to mitigate potential social tensions.