View source for ML-TN-002 - Real-time Social Distancing estimation

==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, malls, 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. 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.

This Technical Note illustrates one of these implementations regarding the real-time social distancing estimation issue. This work started off the publicly-available, open-source Social-Distancing project released by the [https://iit.it/|Istituto Istituto Italiano di Tecnologia (IIT)], which is illustrated in this [https://arxiv.org/abs/2011.02018v2 paper]. The goal was to port the IIT code onto one of the DAVE Embedded Systems [https://wiki.dave.eu/index.php/Main_Page#Single_Board_Computers Single Board Computers] (SBC) suitable to build an industrial-grade automatic machine vision system for social distancing.