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The pervasive transmission of contagious diseases, notably COVID-19, necessitates stringent measures to curb their spread, including SOPs like wearing face masks and maintaining social distancing. This study introduces an innovative system designed to identify individuals in video footage who violate these SOPs. The system operates in phases: detecting individuals in close proximity and identifying those not wearing face masks through facial recognition. Using image processing techniques, it ensures a minimum 2-m separation between individuals via surveillance cameras. The system leverages deep learning to assess SOP compliance, validated on a publicly available face mask detection dataset. This versatile system is applicable to real-time security and surveillance in densely populated environments.

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Transfer Learning-Based Contagious Diseases Transmission Video Surveillance: A COVID-19 Case Study

  • 会议论文

  • 发表日期：

    2025年

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  • S. Muhammad Ahmed Hassan Shah,
  • Asad Ullah,
  • Sana Ullah Jan,
  • Bill Buchanan

摘要

The pervasive transmission of contagious diseases, notably COVID-19, necessitates stringent measures to curb their spread, including SOPs like wearing face masks and maintaining social distancing. This study introduces an innovative system designed to identify individuals in video footage who violate these SOPs. The system operates in phases: detecting individuals in close proximity and identifying those not wearing face masks through facial recognition. Using image processing techniques, it ensures a minimum 2-m separation between individuals via surveillance cameras. The system leverages deep learning to assess SOP compliance, validated on a publicly available face mask detection dataset. This versatile system is applicable to real-time security and surveillance in densely populated environments.