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

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5202
Author(s):  
Manuel Martinez ◽  
Kailun Yang ◽  
Angela Constantinescu ◽  
Rainer Stiefelhagen
Keyword(s):  
Real Time ◽  
Semantic Segmentation ◽  
Physical Distance ◽  
Assistive Devices ◽  
Prototype System ◽  
Blind People ◽  
Audio Feedback ◽  
Daily Lives ◽  
Social Distancing ◽  
Depth Cameras

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.

scholarly journals Smart System to Monitor Social-Distancing During the Covid-19 Pandemic

2020 ◽  
Author(s):  
Omar Elharrouss ◽  
Noor Al-Maadeed ◽  
Khalid Abualsaud ◽  
Amr Mahmoud ◽  
Tamer Khattab ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Computer Vision ◽  
Real Time ◽  
Video Surveillance ◽  
Social Distance ◽  
Moving Objects ◽  
Physical Distance ◽  
Disease Spread ◽  
Smart System ◽  
Social Distancing

We introduce a smart system to track and maintain real-time physical distance between people and to warn people over any deviation from the prescribed distances. Social-distancing is an effective way of slowing infectious disease spread. People are advised to reduce their contacts with each other, thus reducing the chances of transmitting the disease through physical or near contact. We proposed a system to automate the task of tracking social distance using video surveillance and sensors. The system can be used to detect moving objects and measure distance between people. The system collected sensor environmental information for commercial, industrial and governmental purposes. Furthermore we are using drown to detect crowded area. The accuracy of detection using sensors can be helpful when it combined with the camera for computer vision task in terms of visualization using camera and rebuses of detection using sensor. Both camera and sensor gauge the environment to detect moving objects simultaneously.

scholarly journals Real-Time Implementation of AI-Based Face Mask Detection and Social Distancing Measuring System for COVID-19 Prevention

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Safa Teboulbi ◽  
Seifeddine Messaoud ◽  
Mohamed Ali Hajjaji ◽  
Abdellatif Mtibaa
Keyword(s):  
Real Time ◽  
Vision System ◽  
Close Contact ◽  
Public Health Problem ◽  
Confidence Score ◽  
Face Mask ◽  
Physical Distance ◽  
Measuring System ◽  
Social Distancing ◽  
The World

Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem in China and even around the world. This pandemic is having devastating effects on societies and economies around the world. The increase in the number of COVID-19 tests gives more information about the epidemic spread, which may lead to the possibility of surrounding it to prevent further infections. However, wearing a face mask that prevents the transmission of droplets in the air and maintaining an appropriate physical distance between people, and reducing close contact with each other can still be beneficial in combating this pandemic. Therefore, this research paper focuses on implementing a Face Mask and Social Distancing Detection model as an embedded vision system. The pretrained models such as the MobileNet, ResNet Classifier, and VGG are used in our context. People violating social distancing or not wearing masks were detected. After implementing and deploying the models, the selected one achieved a confidence score of 100%. This paper also provides a comparative study of different face detection and face mask classification models. The system performance is evaluated in terms of precision, recall, F1-score, support, sensitivity, specificity, and accuracy that demonstrate the practical applicability. The system performs with F1-score of 99%, sensitivity of 99%, specificity of 99%, and an accuracy of 100%. Hence, this solution tracks the people with or without masks in a real-time scenario and ensures social distancing by generating an alarm if there is a violation in the scene or in public places. This can be used with the existing embedded camera infrastructure to enable these analytics which can be applied to various verticals, as well as in an office building or at airport terminals/gates.

Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. 9-11
Author(s):  
Tomohiro Fukuda
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Computer Games ◽  
Construction Projects ◽  
Mixed Reality ◽  
Semantic Segmentation ◽  
Environment Design ◽  
Aviation Training ◽  
Architecture And Design ◽  
World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

A lightweight network with attention decoder for real-time semantic segmentation

2021 ◽  
Author(s):  
Kang Wang ◽  
Jinfu Yang ◽  
Shuai Yuan ◽  
Mingai Li
Keyword(s):  
Real Time ◽  
Semantic Segmentation

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

scholarly journals Co-Housing Response to Social Isolation of COVID-19 Outbreak, with a Focus on Gender Implications

2021 ◽  
Vol 13 (13) ◽  
pp. 7203
Author(s):  
Emanuele Giorgi ◽  
Lucía Martín Martín López ◽  
Rubén Garnica-Monroy ◽  
Aleksandra Krstikj ◽  
Carlos Cobreros ◽  
...  
Keyword(s):  
Social Isolation ◽  
Social Relations ◽  
Daily Life ◽  
Social Impacts ◽  
Research Projects ◽  
Open Spaces ◽  
Daily Lives ◽  
Social Distancing ◽  
Design Considerations ◽  
Social Habits

COVID-19 forced billions of people to restructure their daily lives and social habits. Several research projects have focused on social impacts, approaching the phenomenon on the basis of different issues and scales. This work studies the changes in social relations within the well-defined urban-territorial elements of co-housing communities. The peculiarity of this research lies in the essence of these communities, which base their existence on the spirit of sharing spaces and activities. As social distancing represented the only effective way to control the outbreak, the research studied how the rules of social distancing impacted these communities. For this reason, a questionnaire was sent to 60 communities asking them to highlight the changes that the emergency imposed on the members in their daily life and in the organization of common activities and spaces. A total of 147 responses were received and some relevant design considerations emerged: (1) the importance of feeling part of a “safe” community, with members who were known and deemed reliable, when facing a health emergency; and (2) the importance of open spaces to carry out shared activities. Overall, living in co-housing communities was evaluated as an “extremely positive circumstance” despite the fact that the emergency worsened socialization.

scholarly journals Real-time Semantic Segmentation with Context Aggregation Network

2021 ◽  
Vol 178 ◽  
pp. 124-134
Author(s):  
Michael Ying Yang ◽  
Saumya Kumaar ◽  
Ye Lyu ◽  
Francesco Nex
Keyword(s):  
Real Time ◽  
Semantic Segmentation
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