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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2020-09, Vol.20 (18), p.5202
• Main Authors: Martinez, Manuel, Yang, Kailun, Constantinescu, Angela, Stiefelhagen, Rainer
• Format: Article
• Language: English
• Subjects: Acoustics; Adult; Algorithms; Artificial Intelligence - statistics & numerical data; Betacoronavirus; Blind people; Blindness; Blindness - psychology; Blindness - rehabilitation; Cameras; Color Vision; Computer Systems - statistics & numerical data; computer vision for the visually impaired; Coronavirus Infections - epidemiology; Coronavirus Infections - prevention & control; Coronaviruses; COVID-19; COVID-19 - prevention & control; Deep learning; Design; Disease control; Disease transmission; Equipment Design; Female; Germany - epidemiology; Headphones; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Male; Pandemics - prevention & control; Physical Distancing; Pneumonia, Viral - epidemiology; Pneumonia, Viral - prevention & control; Real time; Robotics; SARS-CoV-2; Semantic segmentation; Semantics; Sensors; Sensory Aids; Smart Glasses - statistics & numerical data; Social distancing; Visual impairment; Visually Impaired Persons - rehabilitation; Wearable Electronic Devices - statistics & numerical data
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20185202

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.