Validating social distancing through deep learning and VR-based digital twins
27th ACM Symposium on Virtual Reality Software and Technology, VRST 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1596233
ABSTRACT
The Covid-19 pandemic resulted in a catastrophic loss to global economies, and social distancing was consistently found to be an effective means to curb the virus’s spread. However, it is only as effective when every individual partakes in it with equal alacrity. Past literature outlined scenarios where computer vision was used to detect people and to enforce social distancing automatically. We have created a Digital Twin (DT) of an existing laboratory space for remote monitoring of room occupancy and automatically detecting violation of social distancing. To evaluate the proposed solution, we have implemented a Convolutional Neural Network (CNN) model for detecting people, both in a limited-sized dataset of real humans, and a synthetic dataset of humanoid figures. Our proposed computer vision models are validated for both real and synthetic data in terms of accurately detecting persons, posture, and intermediate distances among people. © 2021 Copyright held by the owner/author(s).
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
27th ACM Symposium on Virtual Reality Software and Technology, VRST 2021
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS