During greater part of 2017 and 2018, our team was doing research in Deep Neural Net (DNN) anomaly detection. The problem we were looking to solve was that control centers usually had far too many surveillance video cameras and control panels to monitor and not enough personal to pay attention to them all. Why not apply machine-learning (ML) anomaly detection on camera and computer monitor feeds to alert control center personal of abnormal events as soon as they occur?
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Experiences using this skill are shown below:
This was a student intern project that I took over to turn into a useful application for use within the company. Even though this wasn't a research project, we thought it would be a good way to make Barco Labs better known throughout the company, as many employees viewed us as an "ivory tower" doing esoteric research of little practical value. The Smart Meeting Room App (SMRA) had the very practical benefit of finding and scheduling meeting rooms on the Barco campus.
By 2019, our machine-learning research project was now integrating and managing multiple cameras and video sources and as a result it was becoming increasingly difficult to configure using config. files only. I was given the task of creating a professional-looking desktop UI that could be accessed from a web browser on company locked-down PCs. It was decided that there would be no support for mobile devices and the GUI would be package as a Docker image.
Developed several browser-based video playback and video device management applications. Some examples (in reverse chronological order):