Abstract
The outbreak of COVID-19 has resulted in many different policies being adopted across the world to reduce the spread of the virus. These policies include wearing surgical masks, hand hygiene practices, increased social distancing and full country-wide lockdown. Specifically, social distancing involves keeping a certain distance from others and avoiding gathering together in large groups. Automatic crowd density estimation is a technological solution that could help in guaranteeing social distancing by reducing the probability that two persons in a public area come in close proximity to each other while moving around. This paper proposes a novel low complexity RF sensing system for automatic people counting based on low cost UWB transceivers. The proposed system is based on an ordinary classifier that exploits features extracted from the channel impulse response of UWB communication signals. Specifically, features are extracted from the sorted list of singular values obtained from the singular value decomposition applied to the matrix of the channel impulse response vector differences. Experimental results achieved in two different environments show that the proposed system is a promising candidate for future automatic crowd density monitoring systems.
Highlights
Automatic crowd density estimation is a technological solution that could help in guaranteeing social distancing by reducing the probability that two persons in a public area come in close proximity to each other while moving around
This paper shows the feasibility of a novel non-radar approach, based on commercial low cost UWB transceivers, from which it is possible to extract the Channel Impulse Response (CIR)
This paper presents a novel UWB-based people counting system, which uses the CIR extracted from low cost commercial UWB transceivers, more commonly used for localization purposes
Summary
Social distancing is an effective approach to limit disease diffusion [1]. During the ongoing COVID-19 pandemic, several social distancing measures have been implemented by governments: travel restrictions, border control, closing public places and keeping a. This technology is based on the fact that RF propagation channel is continuously modified by the presence of people This in turns means that it is possible to analyse RF signals to identify changes produced by the presence of people and extract information on the number of persons inside the monitored environment. Most of these device-free approaches are based on WiFi signals [11,12] but some recent works have proved the possibility to use Long Term Evolution (LTE) signals for radio analytics applications [13,14].
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