Public Safety and Security with Machine Learning at the Edge

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There are wide and varied applications for machine learning; however, few are as compelling as those in public safety. Recently, I worked with the team from Arcturus who have been working on methods to provide operational insights and improve public safety for smart public transportation systems during NXP Connects in Santa Clara.

The application that Arcturus focuses on processes video from a security camera on a subway platform and looks for three conditions;

  1. how crowded the platform is
  2. how close to the platform’s edge people are standing
  3. if there are any abandoned packages or luggage

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If one of these conditions are met, the system immediately provides a notification or performs a local, real-time action—such as shutting down track power. The real-time response really illustrates the power that machine learning has to transform a task that would typically be left to human observation to turn it into active detection.

Built with i.MX 8M Mini

To build the system, Arcturus chose NXP’s quad core i.MX 8M Mini applications processor combined with ArmNN to run their neural network detection algorithm at the edge. For a public safety system like this one, edge processing improves response time and reliability by eliminating the need to ship pixel data from each camera across the network and up to the cloud.

The table below describes the efficiency Arcturus was able to achieve using Arm NN, when compared to their existing pipeline deployed with OpenCV—the performance improvement allowed them to run their detection natively on the four Arm® A53 cores in the i.MX8M Mini, without the need for specialized ML hardware or even a GPU.

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Supported by eIQ Software Development Tool

To help Arcturus port their application from OpenCV to the i.MX 8M Mini, we let them loose on a pre-production version of our eIQ machine learning software development tool. It’s important to note that eIQ also supports OpenCV for machine learning, but we’ll typically recommend using Arm NN for performance and efficiency reasons. Using eIQ software helped them rapidly deploy their application in days, rather than weeks without any direct support from us. From our perspective, this project offered a good exercise to ensure that eIQ machine learning development environment could deliver on the promise of a smooth transition from desktop-to-embedded application and that it offers users full advantage of the processing available in the i.MX 8M Mini device.

We’re excited to see what Arcturus has forthcoming. They have work underway to apply ML techniques that use clothing characteristics and reidentification methods to actively locate people. Therefore, losing your child at the mall may just become a thing of the past! If you need more information, contact them at ArcturusNetworks.

For more information on eIQ and i.MX8M Mini

Unlock Machine Learning on Edge Devices with eIQ Software Development Environment

Tags: Technologies

Author

Markus Levy

Markus Levy

Director of AI and Machine Learning Technologies, NXP Semiconductor

Markus Levy joined NXP in 2017 as the Director of AI and Machine Learning Technologies. In this position, he is focused primarily on the technical strategy, roadmap, and marketing of AI and machine learning capabilities for NXP's microcontroller and i.MX applications processor product lines. Previously, Markus was chairman of the board of EEMBC, which he founded and ran as the President since April 1997. Mr. Levy was also president of the Multicore Association, which he co-founded in 2005. Previously, he was senior analyst at Microprocessor Report and an editor at EDN magazine. Markus began his career at Intel Corporation, as both a senior applications engineer and customer training specialist for Intel's microprocessor and flash memory products. Markus volunteered for thirteen years as a first responder–fighting fires and saving lives.