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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 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 is built around a 4-core ARM Cortex A53 CPU and has a rich set of peripherals and systems. It also integrates a 2.3 TOPS Neural Processing Unit (NPU) and native interfaces to connect image sensors making it very suited for this applicationkind of applications.
==The hardware/software platform==
As stated previously, the main application derives from the IIT Social-Distancing project. It was developed in several steps starting when only a few alpha samples of the i.MX8M Plus were available.
The first step was conducted using the official evaluation kit (EVK) by NXP. Its goal was to make the Social-Distancing project to work on the new platform maintaining the core functionalities. In essence, the code was modified in order to replace the [https://github.com/CMU-Perceptual-Computing-Lab/openpose OpenPose library] with [https://github.com/tensorflow/tfjs-models/tree/master/posenet PoseNet]. This was required to cope with the operations actually supported by the [https://www.nxp.com/design/software/development-software/eiq-ml-development-environment:EIQ NXP eIQ] software stack and the NPU. For those who are familiar with embedded software development, this should be unsurprising as . When porting applications from PC-like platforms to embedded platforms, handling such hardware/software constraints is a common practice.
The resultingprocessing pipeline is shown in the following figure. The following screenshots show the application running on the EVK. [[File:Social-distancing-screenshot1.png|center|thumb|600x600px|caption]] [[File:Social-distancing-screenshot2.png|center|thumb|600x600px|c]]
== Testing and results ==
