Developing an Automated Vision System for Maintaing Social Distancing to Cure the Pandemic

Authors

  • Fatima Hardan Department of Mechatronics Engineering / Al-Khwarizmi College of Engineering University of Baghdad
  • Ahmed R. J. Almusawi Department of Mechatronics Engineering / Al-Khwarizmi College of Engineering University of Baghdad

DOI:

https://doi.org/10.22153/kej.2022.03.002

Abstract

The world is currently facing a medical crisis. The epidemic has affected millions of people around the world since its appearance. This situation needs an urgent solution. Most countries have used different solutions to stop the spread of the epidemic. The World Health Organization has imposed some rules that people should adhere. The rules are such, wearing masks, quarantining infected people and social distancing. Social distancing is one of the most important solutions that have given good results to confront the emerging virus. Several systems have been developed that use artificial intelligence and deep learning to track social distancing. In this study, a system based on deep learning has been proposed. The system includes monitoring and detecting people besides measuring the social distance between them. The proposed system consists of two parts: (1) detecting the faces of people using the Viola-Jones algorithm. The Cascade classifiers were trained. The Cascade classifiers used in the algorithm with feature descriptors to detect side faces and wear masks. Hence, training is dominant for detection. (2) measurement of the Euclidean distance between the centers of the rectangles of the people who were revealed in the first part. The distance between individuals' is measured to check how well they adhere to social distancing. The results revealed that the proposed system can perform well in applying images to track the distance between people.

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Published

2022-03-19

How to Cite

Hardan, F., & R. J. Almusawi, A. . (2022). Developing an Automated Vision System for Maintaing Social Distancing to Cure the Pandemic. Al-Khwarizmi Engineering Journal (Alkej), 18(1), 38–50. https://doi.org/10.22153/kej.2022.03.002