Early Warning System for Physical Distancing Detection in the Prevention of COVID-19 Spread

The widespread of the COVID-19 virus poses a threat to human health; hence, the prevention of the Covid-19 spread needs to immediately be realized. Physical distancing is a way that can reduce the COVID-19 spread. However, human negligence in implementing physical distancing due to the lack of strict supervision often occurs. Many studies, one of which is Camera Machine learning-based system, have attempted to solve this problem, but they focused on detection accuracy without considering device mobility that, in fact, is needed to detect physical distancing. We proposed a system that can provide early warnings against physical distancing negligence on the constrained computers. The system was built using a constrained computer and a camera with high mobility to facilitate its movement. The system used Tensorflow Lite as a framework to do machine learning and the SSD MobileNet pretrain model was used to classify human detection. Test scenarios applied included distance accuracy and detection accuracy. The system had accuracy in detecting physical distancing negligence with 86% accuracy and 87% F -1 Score. The system built can run on a constrained computer by used 4.59 MB of memory that is 0.001 % of total memory, and the cumulative utilization of four cores was 139%. The system performs 10^%better in accuracy than a similar related work.