People counting on low cost embedded hardware during the sars-cov-2 pandemic

ABSTRACT

Detecting and tracking people is a challenging task in a persistent crowded environment as retail, airport or station, for human behaviour analysis of security purposes. Especially during the global spread of SARS-CoV-2 virus that has become part of everyday life in every country, it is important to be able to manage the flows inside and outside buildings indoors. This article introduces an approach to detect and count people when they cross a virtual line. The methods used are based on deep learning and in particular on convolutional neural networks, specifically MobileNetV3 which is used for the detection task and MOSSE filter which is used for the tracking phase. The hardware system assembled for people counting is inexpensive, as it is formed by Raspberry Pi4 and a Picamera module v2. These devices have already been installed in some supermarkets and museums in the center of Italy, precisely in the area of the Marche region. © Springer Nature Switzerland AG 2021.