Accessing Passersby Proxemic Signals through a Head-Worn Camera: Opportunities and Limitations for the Blind
Abstract
The spatial behavior of passersby can be critical to blind individuals to initiate interactions, preserve personal space, or practice social distancing during a pandemic. Among other use cases, wearable cameras employing computer vision can be used to extract proxemic signals of others and thus increase access to the spatial behavior of passersby for blind people. Analyzing data collected in a study with blind (N=10) and sighted (N=40) participants, we explore: (i) visual information on approaching passersby captured by a head-worn camera; (ii) pedestrian detection algorithms for extracting proxemic signals such as passerby presence, relative position, distance, and head pose; and (iii) opportunities and limitations of using wearable cameras for helping blind people access proxemics related to nearby people. Our observations and findings provide insights into dyadic behaviors for assistive pedestrian detection and lead to implications for the design of future head-worn cameras and interactions.
BibTeX
@conference{Lee-2021-134073,author = {Kyungjun Lee, Daisuke Sato, Saki Asakawa, Chieko Asakawa, Hernisa Kacorri},
title = {Accessing Passersby Proxemic Signals through a Head-Worn Camera: Opportunities and Limitations for the Blind},
booktitle = {Proceedings of 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '21)},
year = {2021},
month = {October},
pages = {1 - 15},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
keywords = {blind people, proxemics, wearable camera, machine learning, pedestrian detection, spatial proximity},
}