Skip to main content
Top
Published in: New Generation Computing 1/2023

02-01-2023

An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic

Authors: Santosh Kumar Sahoo, G. Palai, Baraa Riyadh Altahan, Sk Hasane Ahammad, P. Poorna Priya, Md.Amzad Hossain, Ahmed Nabih Zaki Rashed

Published in: New Generation Computing | Issue 1/2023

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human ‘person class’ towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Brodeur, A., Islam, A., Gray, D., Bhuiyan, S. J.: “A Literature Review of the Economics of COVID-19”, Discussion paper series, IZA- Institute of Labor Economics, DP No. 13411, pp. 1–61 (2020). Brodeur, A., Islam, A., Gray, D., Bhuiyan, S. J.: “A Literature Review of the Economics of COVID-19”, Discussion paper series, IZA- Institute of Labor Economics, DP No. 13411, pp. 1–61 (2020).
2.
3.
go back to reference Ahmed, I., Ahmad, M., Jeon, G.: Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic. Sustain. Cities Soc. 69(102777), 1–11 (2021) Ahmed, I., Ahmad, M., Jeon, G.: Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic. Sustain. Cities Soc. 69(102777), 1–11 (2021)
4.
go back to reference Ahmed, I., Ahmad, M., Rodrigues, J.J.P.C., et al.: A deep learning-based social distance monitoring framework for COVID-19. Sustain. Cities Soc. 65(102571), 1–12 (2021) Ahmed, I., Ahmad, M., Rodrigues, J.J.P.C., et al.: A deep learning-based social distance monitoring framework for COVID-19. Sustain. Cities Soc. 65(102571), 1–12 (2021)
5.
go back to reference Payedimarri, A.B., Concina, D., Portinale, L., Canonico, M., et al.: Prediction models for public health containment measures on COVID-19 using artificial intelligence and machine learning: a systematic review. Int. J. Environ. Res. Public Health 18(4499), 1–11 (2021) Payedimarri, A.B., Concina, D., Portinale, L., Canonico, M., et al.: Prediction models for public health containment measures on COVID-19 using artificial intelligence and machine learning: a systematic review. Int. J. Environ. Res. Public Health 18(4499), 1–11 (2021)
6.
go back to reference Rahim, A., Maqbool, A., Rana, T.: Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera. PLoS ONE 16(2), e0247440 (2021)CrossRef Rahim, A., Maqbool, A., Rana, T.: Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera. PLoS ONE 16(2), e0247440 (2021)CrossRef
7.
go back to reference Tuli, S., Tuli, S., Tuli, R., Gill, S.S.: Predicting the growth and trend of covid-19 pandemic using machine learning and cloud computing. Internet Things 11, 100222 (2020)CrossRef Tuli, S., Tuli, S., Tuli, R., Gill, S.S.: Predicting the growth and trend of covid-19 pandemic using machine learning and cloud computing. Internet Things 11, 100222 (2020)CrossRef
8.
go back to reference Pooranam, N., Priya, S.P.N., Sruthi, S., Dhanya, S.K.: A safety measuring tool to maintain social distancing on COVID-19 using deep learning approach. ICCCEBS J. Phys. Conf. Series 1916(012122), 1–8 (2021) Pooranam, N., Priya, S.P.N., Sruthi, S., Dhanya, S.K.: A safety measuring tool to maintain social distancing on COVID-19 using deep learning approach. ICCCEBS J. Phys. Conf. Series 1916(012122), 1–8 (2021)
9.
go back to reference Vinitha, V., Velantina, V.: Social distancing detection system with artificial intelligence using computer vision and deep learning. Int. Res. J. Eng. Technol. (IRJET) 7(8), 4049–4053 (2020) Vinitha, V., Velantina, V.: Social distancing detection system with artificial intelligence using computer vision and deep learning. Int. Res. J. Eng. Technol. (IRJET) 7(8), 4049–4053 (2020)
10.
go back to reference Pandian, K: Next-generation network - global market trajectory & analytics. J. Next Gener. Comput. Syst. 33(2), 1–20 (2020) Pandian, K: Next-generation network - global market trajectory & analytics. J. Next Gener. Comput. Syst. 33(2), 1–20 (2020)
11.
go back to reference Rezaei, M., Azarmi, M.: DeepSOCIAL: social distancing monitoring and infection risk assessment in COVID-19 pandemic. Appl. Sci. 10(7514), 1–29 (2020) Rezaei, M., Azarmi, M.: DeepSOCIAL: social distancing monitoring and infection risk assessment in COVID-19 pandemic. Appl. Sci. 10(7514), 1–29 (2020)
12.
go back to reference Saponara, S., Elhanashi, A., Gagliardi, A.: Implementing a real-time, AI based, people detection and social distancing measuring system for Covid-19. J. Real-Time Image Process. 18(6), 1937–1947 (2021)CrossRef Saponara, S., Elhanashi, A., Gagliardi, A.: Implementing a real-time, AI based, people detection and social distancing measuring system for Covid-19. J. Real-Time Image Process. 18(6), 1937–1947 (2021)CrossRef
13.
go back to reference Arya, S., Patil, L., Wadeganokar, A., et al.: Study of various measure to monitor social distancing using computer vision: a review. Int. J. Eng. Res. Technol. (IJERT) 10(5), 329–326 (2021) Arya, S., Patil, L., Wadeganokar, A., et al.: Study of various measure to monitor social distancing using computer vision: a review. Int. J. Eng. Res. Technol. (IJERT) 10(5), 329–326 (2021)
14.
go back to reference Yang, D., Yurtsever, E., Renganathan, V., et al.: A vision-based social distancing and critical density detection system for COVID-19. Sensors 21(4608), 1–15 (2021) Yang, D., Yurtsever, E., Renganathan, V., et al.: A vision-based social distancing and critical density detection system for COVID-19. Sensors 21(4608), 1–15 (2021)
15.
go back to reference Wen, H., Huang, C., Guo, S.: The application of convolutional neural networks (CNNs) to recognize defects in 3D-printed parts. Materials 14(2575), 1–14 (2021) Wen, H., Huang, C., Guo, S.: The application of convolutional neural networks (CNNs) to recognize defects in 3D-printed parts. Materials 14(2575), 1–14 (2021)
Metadata
Title
An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic
Authors
Santosh Kumar Sahoo
G. Palai
Baraa Riyadh Altahan
Sk Hasane Ahammad
P. Poorna Priya
Md.Amzad Hossain
Ahmed Nabih Zaki Rashed
Publication date
02-01-2023
Publisher
Springer Japan
Published in
New Generation Computing / Issue 1/2023
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-022-00202-1

Other articles of this Issue 1/2023

New Generation Computing 1/2023 Go to the issue

Premium Partner