  COVID-19 is a disease caused by severe respiratory syndrome coronavirus. It
was identified in December 2019 in Wuhan, China. It has resulted in an ongoing
pandemic that caused infected cases including some deaths. Coronavirus is
primarily spread between people during close contact. Motivating to this
notion, this research proposes an artificial intelligence system for social
distancing classification of persons by using thermal images. By exploiting
YOLOv2 (you look at once), a deep learning detection technique is developed for
detecting and tracking people in indoor and outdoor scenarios. An algorithm is
also implemented for measuring and classifying the distance between persons and
automatically check if social distancing rules are respected or not. Hence,
this work aims at minimizing the spread of the COVID-19 virus by evaluating if
and how persons comply with social distancing rules. The proposed approach is
applied to images acquired through thermal cameras, to establish a complete AI
system for people tracking, social distancing classification, and body
temperature monitoring. The training phase is done with two datasets captured
from different thermal cameras. Ground Truth Labeler app is used for labeling
the persons in the images. The achieved results show that the proposed method
is suitable for the creation of a smart surveillance system in smart cities for
people detection, social distancing classification, and body temperature
analysis.
