Camera-based Human Counting for COVID-19 Capacity Restriction

In this paper, a Human Counting system is implemented for COVID-19 capacity restrictions. It was implemented using the deep learning model You Only Look Once version 3(YOLOv3) to detect and count the people in a room. The system also can monitor the social distancing between the people in the room while labeling each person as “safe” or “unsafe” depending on whether they respect the social distancing protocols that the World Health Organization recommended or not. To make the project user friendly, a Graphical User Interface (GUI) was implemented to allow the user to choose the source of their images that will be used as input to be processed by the system. An experiment was carried out to evaluate the performance of the system under different conditions and in different scenarios where the evaluation was done according to some metrics such as accuracy, precision and recall. The output results from this experiment were demonstrated in details and compared to a similar algorithm as both algorithms focused on people detection using images from an inclined camera. The results show an accuracy of 96% for detection and the number of people counted.