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A Deep-Learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1576))

Abstract

The contagious Corona Virus (COVID-19) transmission can be reduced by following and maintaining physical distancing (also known as COVID-19 social distance). The World Health Organisation (WHO) recommends it to prevent COVID-19 from spreading in public areas. On the other hand, people may not be maintaining the required 2-m physical distance as a mandated safety precaution in shopping malls and public places. The spread of the fatal disease may be slowed by an active monitoring system suitable for identifying distances between people and alerting them. This paper introduced a deep learning-based system for automatically detecting physical distance using video from security cameras. The proposed system employed the fine-tuning YOLO v4 for object detection and classification and Deepsort for tracking the detected people using bounding boxes from the video. Pairwise L2 vectorized normalization was utilized to generate a three-dimensional feature space for tracking physical distances and the violation index, determining the number of individuals who follow the distance rules. For training and testing, we use the MS COCO and Oxford Town Centre (OTC) datasets. We compared the proposed system to two well-known object detection models, YOLO v3 and Faster RCNN. Our method obtained a weighted mAP score of 87.8% and an FPS score of 28; both are computationally comparable.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey

    Masum Shah Junayed

  2. Department of CSE, Daffodil International University, Dhaka, Bangladesh

    Masum Shah Junayed

  3. College of Data Science and Engineering, American University of Malta, Bormla, Malta

    Md Baharul Islam

Authors
  1. Masum Shah Junayed
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  2. Md Baharul Islam
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Correspondence to Masum Shah Junayed .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Central University of Karnataka, Gulbarga, India

    Ravindra Hegadi

  3. Indian Statistical Institute, Kolkata, India

    Umapada Pal

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Junayed, M.S., Islam, M.B. (2022). A Deep-Learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_19

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  • DOI: https://doi.org/10.1007/978-3-031-07005-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07004-4

  • Online ISBN: 978-3-031-07005-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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