A trio of researchers at Carnegie Mellon University has taken the use of WiFi signals to identify people in a building to a new level, through the use of a deep neural network. Jiaqi Geng, Dong Huang and Fernando De la Torre suggest, in a paper they have posted to the arXiv preprint server, that their approach allows for creating images on par with RGB cameras.
Back in 2013, a team of engineers at MIT found that WiFi signals could be used to detect the presence of a person in a building. They noted that by mapping the signals over time, they could see where the signals were being blocked by a person's body. By continuing the process over the next few years, they found that they were able to create stick figures that showed where a person was in a given building at any given time.
The process is now known as DensePose. In this new effort, the research trio have taken this approach to a new level by introducing a neural network that helps fill in the bodies of the stick figures, providing much more lifelike images—and it can do it on the fly, allowing for real-time motion tracking of multiple people in a given area.
To read more, click here.