mmDetect: YOLO-Based Processing of mm-Wave Radar Data for Detecting Moving People

The application of millimeter-wave (mmWave) Radar sensors for people monitoring raised a lot of interest in the context of active assisted living (AAL) since the processing of Radar signals can provide interesting information about the observed subjects. Correct recognition of the ongoing behavior, however, cannot disregard from detecting where the subject is positioned. Detection approaches, based on constant false alarm rate (CFAR) algorithms, sometimes fail to correctly identify the presence of targets within the observed scenario, especially in complex environments such as indoors. This article proposes the use of an mmWave multiple-input multiple-output (MIMO) Radar in combination with a you only look once (YOLO) neural-network-based algorithm for the detection of moving people in indoor environments by processing all the data cube information at the same time. Results are validated through experimental tests, which involve subjects walking in linear or random mode, different Radar configurations, and different indoor environments. By exploiting at the same time information such as the angle, Doppler, and range distance of the target, the proposed approach proves to be very effective in the examined scenarios. The experimental results will be discussed in this work to demonstrate the effectiveness of the proposed method.