Citation

Lee K, Sato D, Asakawa S, Asakawa C, Kacorri H. ASSETS 2021; 21: e3441852.3471232.

Copyright

(Copyright © 2021, Association for Computing Machinery)

DOI

10.1145/3441852.3471232

PMID

35187543

PMCID

PMC8855357

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.

Language: en

Keywords

machine learning; pedestrian detection; Accessibility technologies; blind people; Computing methodologies → Computer vision; Empirical studies in accessibility; Empirical studies in HCI; Human-centered computing → User studies; Mobile devices; proxemics; spatial proximity; wearable camera