Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

Muhammad Haris Kaka Khel (Electrical Section, Universiti Kuala Lumpur British Malaysian Institute, 53100,)
Kushsairy Kadir (Electrical Section, Universiti Kuala Lumpur British Malaysian Institute, 53100,)
Waleed Albattah (Department of Information Technology, College of Computer, Qassim University, Buraydah,)
Sheroz Khan (Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Qassim,)
MNMM Noor (Computer Engineering Section, Universiti Kuala Lumpur Malaysian Institute of Information Technology, Kuala Lumpur, 50250,)
Haidawati Nasir (Computer Engineering Section, Universiti Kuala Lumpur Malaysian Institute of Information Technology, Kuala Lumpur, 50250,)
Shabana Habib (Department of Information Technology, College of Computer, Qassim University, Buraydah,)
Muhammad Islam (Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Qassim,)
Akbar Khan (Electrical Section, Universiti Kuala Lumpur British Malaysian Institute, 53100,)

Emerging Science Journal
Vol 5 (2021): Special Issue "COVID-19: Emerging Research"

Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%. Doi: 10.28991/esj-2021-SPER-14 Full Text: PDF