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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 describes 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 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 a DAVE Embedded Systems Single Board Computer (SBC) powered by the [[https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-8-processors/i-mx-8m-plus-arm-cortex-a53-machine-learning-vision-multimedia-and-industrial-iot:IMX8MPLUS|NXP i.MX8M Plus SoC]]. This industrial/automotive-grade SoC has a rich set of peripherals and systems. It also integrates a 2.3 TOPS Neural Processing Unit (NPU) and native interfaces to connects connect image sensors making it a very suited component for this application.
==The hardware/software platform==
