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Dmitri De Vaz

Privacy-friendly People Counting using Machine Learning

Block diagram

Objective #

The goal of this project was to use machine learning to develop a self-contained (no cloud) people counting system using only low-cost and privacy friendly sensors (i.e. no cameras, no radar, etc.) that runs on a low power, ceiling mounted sensor cluster.

I came up with the proof-of-concept, pitched it to management, and then led the development and successful deployment of this new type of occupancy estimation system.

The technology eventually led to a patent application. You can read more about the project in this whitepaper that I authored, which gives an overview of the technology and it's applications.

This was a huge project that I really enjoyed working on. There's something about bringing new technology, all the way from ideation to production that is very fulfilling to me. This project had many challenges but in the end the feature delivered as it was an industry-first privacy-friendly people counting solution at this price point.

My Role #

As the senior ML engineer and technical lead for this project, I was responsible for the following:

Impact #