==Introduction==
Because of the Covid-19 pandemic, everyone has learned to deal with the so-called "Social Distancing" rules very well. When it comes to spaces shared by many people — such as squares, public or private offices, open-spacesmalls, etc. - — it is not easy to monitor in real-time the compliance with these rules.
Automatic systems that are capable to do the job have been developed. Most of them are implemented as software running on camera-equipped PC's making use of visual techniques. Because of the nature of the problem, this is not a one-size-fits-all solution, however. In many cases, the use of a properly designed embedded platform is mandatory, for example, because of tight space constraints, harsh environment operability, or cost constraints — requirements that are typical for industrial-grade applications.
To date, though, the computing power required for algorithms that complex has represented a hurdle difficult to overcome, hindering the adoption of embedded platforms for these tasks. Recently, new system-on-chips (SoC's) integrating Neural Network hardware accelerators have appeared on the market, however. Thanks to such an improvement in terms of computational power, these devices allow the implementation of novel solutions satisfying all the above-mentioned requirements.
Tuttavia, la recente introduzione di acceleratori hardware per reti neurali integrati rende ora possibile la messa a punto di dispositivi dotati di tutti i requisiti richiestiThis Technical Note describes one of these implementations regarding the real-time social distancing estimation issue.Nel corso della presentazione, viene illustrato un caso di questo tipo. A partire dal lavoro svolto dall'Isituto This work started off the publicly-available open-source Social-Distancing project released by the [[Istituto Italiano di Tecnologia (IIT)|https://iit.it/]], viene descritta l'attività svolta per il trasportare il software sviluppato da which is illustrated in this [[paper|https://arxiv.org/abs/2011.02018v2]]. The goal was to port the IIT code onto a DAVE Embedded Systems Single Board Computer (SBC) powered by the [[https://githubwww.nxp.com/IITproducts/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-8-PAVISprocessors/Sociali-mx-8m-plus-arm-cortex-a53-machine-learning-vision-multimedia-Distancing) su una piattaforma embedded equipaggiata con il systemand-onindustrial-chip (SoC) iot:IMX8MPLUS|NXP i.MX8M PlusSoC]]. This industrial/automotive-grade SoC has a rich set of peripherals and systems. It also integrates a 2. Questo componente, oltre ad un processore multicore ARM Cortex A53 ed una serie di interfacce native per il collegamento di sensori di immagine e telecamere, è dotato di un acceleratore per reti neurali 3 TOPS Neural Processing Unit (NPU) ottimizzato per le reti di tipo convoluzionale (CNN), cioè quelle più comunemente utilizzate per l'elaborazione di immagini e flussi video. Tutte queste caratteristiche lo rendono quindi un validissimo candidato per lo sviluppo di prodotti destinati ad applicazioni come quella del Social Distancingand native interfaces to connects image sensors making it a very suited component for this application. ==The hardware/software platform==
