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Deep Watcher: A Surveillance System Using Deep Learning for the COVID-19 Pandemic

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Advances in Computing and Data Sciences (ICACDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1848))

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Abstract

Several solutions have been proposed to combat the COVID-19 pandemic. In the absence or limited availability of medical resources, World Health Organization has recommended several safety measures. These measures were proposed to control the infection rate and keep current medical resources from depleting. Non-pharmaceutical intervention strategies such as wearing a mask and maintaining social distance are still being employed to combat the COVID-19 sickness. To contribute to this idea of human safety, our work aims to develop a model for detecting non-mask faces quickly and people who are not maintaining social distance in public. The proposed model uses computer vision and artificial intelligence to detect masks and distance between people. Also, a proposition has been made to increase localization performance during detection using the bounding box transformation. The combination of face mask detection and the social distance detection paradigm suggested in this paper is ideal for video surveillance equipment.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    Rohil Kulshreshtha

  2. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    J. Jayapradha

Authors
  1. Rohil Kulshreshtha
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  2. J. Jayapradha
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Corresponding author

Correspondence to J. Jayapradha .

Editor information

Editors and Affiliations

  1. Consilio Research Lab, Tallinn, Estonia

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P.K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Kulshreshtha, R., Jayapradha, J. (2023). Deep Watcher: A Surveillance System Using Deep Learning for the COVID-19 Pandemic. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_5

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  • DOI: https://doi.org/10.1007/978-3-031-37940-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37939-0

  • Online ISBN: 978-3-031-37940-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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