Difference between revisions of "ML-TN-002 - Real-time Social Distancing estimation"
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==Introduction== | ==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, | + | 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 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. | + | 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. | + | 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 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 [[Istituto Italiano di Tecnologia (IIT)|https://iit.it/]], 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://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 image sensors making it a very suited component for this application. | |
| − | + | ||
| + | ==The hardware/software platform== | ||
Revision as of 11:01, 27 January 2021
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History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | February 2021 | First public release |
Introduction[edit | edit source]
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 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 describes 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/, which is illustrated in this 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 [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 image sensors making it a very suited component for this application.
