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DOI: 10.3724/SP.J.2096-5796.21.00030

Virtual-reality-based digital twin of office spaces with social distance measurement feature

Abstract

Background  Social distancing is an effective way to reduce the spread of the SARS-Covid2 virus. Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing. However, because of the present lockdown measures in several countries, the validation of computer vision systems using large-scale datasets is a challenge. Methods  In this paper, a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality (VR) technology. Using VR, we modeled a digital twin (DT) of an existing office space and used it to create a dataset of individuals in different postures, dresses, and locations. To test the proposed solution, we implemented a convolutional neural network (CNN) model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures. Results  We detected the number of persons in both the real and synthetic datasets with more than 90% accuracy, and the actual and measured distances were significantly correlated (r=0.99). Finally, we used intermittent-layer- and heatmap-based data visualization techniques to explain the failure modes of a CNN. Conclusions  A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals. The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed.

Cite this article

Abhishek MUKHOPADHYAY, G S Rajshekar REDDY, KamalPreet Singh SALUJA, Subhankar GHOSH, Anasol PEÑA-RIOS, Gokul GOPAL, Pradipta BISWAS. Virtual-reality-based digital twin of office spaces with social distance measurement feature. Virtual Reality & Intelligent Hardware DOI:10.3724/SP.J.2096-5796.21.00030