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Research Article

EEO. 2021; 20(2): 844-852


AN IOT BASED SOCIAL DISTANCING MONITORING SYSTEM IN PUBLIC AREA FOR REDUCING THE IMPACT OF COVID-19

Aamir Nizam Ansari, Buhsra Shakeel, Dr. Rami N. Alkhawaji, Abdulellah A. Alaboudi, Aariz Nizam Ansari.




Abstract

The massive pandemic of the 2019 novel influenza virus, identified as COVID-19 by the World Health Organization (WHO), has put hundreds of governments all over the world in jeopardy. Nearly every single country on the planet has been highly worried about the COVID-19 virus outbreak. To avoid the spread of this disease, we must thoroughly protect ourselves with sufficient measures. Fear of the health consequences of Marburg and Ebola has led some countries to make bad decisions, such as changing their basic health care systems and partially or completely discontinuing some medical procedures, though some have made choices. If communicable diseases such as cold sores, chicken pox, and influenza virus are to be avoided, social distance is demanded. We minimize the probability of catching and spreading the disease to everyone else in the community by holding people away from one another. Since its inception, the pandemic has been rapidly exploited by various scientific communities, and IoT is one of the pioneers in this field, using a broad variety of technologies to combat this global threat. The IoT procedure was used in the course of the respective COVID-19 clinical therapies to "reduce" COVID-19 distributed to someone else as the patient supervision following a diagnosis of the disorder in compliance with the "liability" of the relevant "devices" and "applications." At the moment, we'd like the pedestrian counting method to rely on open computational vision and artificial intelligence rather than manual measurement. There are small, concealed cameras installed at the scene, and police can obtain footage from these clips to closely track the nature of the incident. This also applies to drones, which can now use videos as testimony.

Key words: COVID-19, IoT, OpenCV, Social Distancing, Deep Learning, Computer Vision






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