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ML-TN-002 - Real-time Social Distancing estimation

38 bytes removed, 15:47, 27 January 2021
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. Because of the nature of the problem, this 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.
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