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Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

Martinez, Manuel; Yang, Kailun; Constantinescu, Angela; Stiefelhagen, Rainer

Abstract:
The current COVID-19 pandemic is having a major impact on our daily lives. Social distancing is one of the measures that has been implemented with the aim of slowing the spread of the disease, but it is difficult for blind people to comply with this. In this paper, we present a system that helps blind people to maintain physical distance to other persons using a combination of RGB and depth cameras. We use a real-time semantic segmentation algorithm on the RGB camera to detect where persons are and use the depth camera to assess the distance to them; then, we provide audio feedback through bone-conducting headphones if a person is closer than 1.5 m. Our system warns the user only if persons are nearby but does not react to non-person objects such as walls, trees or doors; thus, it is not intrusive, and it is possible to use it in combination with other assistive devices. We have tested our prototype system on one blind and four blindfolded persons, and found that the system is precise, easy to use, and amounts to low cognitive load.

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Verlagsausgabe §
DOI: 10.5445/IR/1000126916
Veröffentlicht am 28.11.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000126916
Erschienen in Sensors
Band 20
Heft 18
Seiten Art. Nr.: 5202
Vorab online veröffentlicht am 12.09.2020
Schlagwörter computer vision for the visually impaired, social distancing, semantic segmentation
Nachgewiesen in Scopus
Web of Science
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