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COVID-19 is a disease caused by severe respiratory syndrome coronavirus. Itwas identified in December 2019 in Wuhan, China. It has resulted in an ongoingpandemic that caused infected cases including some deaths. Coronavirus isprimarily spread between people during close contact. Motivating to thisnotion, this research proposes an artificial intelligence system for socialdistancing classification of persons by using thermal images. By exploitingYOLOv2 (you look at once), a deep learning detection technique is developed fordetecting and tracking people in indoor and outdoor scenarios. An algorithm isalso implemented for measuring and classifying the distance between persons andautomatically check if social distancing rules are respected or not. Hence,this work aims at minimizing the spread of the COVID-19 virus by evaluating ifand how persons comply with social distancing rules. The proposed approach isapplied to images acquired through thermal cameras, to establish a complete AIsystem for people tracking, social distancing classification, and bodytemperature monitoring. The training phase is done with two datasets capturedfrom different thermal cameras. Ground Truth Labeler app is used for labelingthe persons in the images. The achieved results show that the proposed methodis suitable for the creation of a smart surveillance system in smart cities forpeople detection, social distancing classification, and body temperatureanalysis.

People detection and social distancing classification in smart cities for COVID-19 by using thermal images and deep learning algorithms