Novel Robust On-Line Indoor Occupancy Counting System Using mmWave Radar

Abstract

In this work, a novel robust on-line indoor occupancy counting approach is proposed using the millimeter wave (mmWave) frequency-modulated continuous-wave (FMCW) radar. The acquired radar data can be represented as sparse three-dimensional (3D) radar point-clouds. The conventional indoor occupancy-counting schemes suffer from various types of errors and uncertainties emerging from mmWave radar sensors. Thus, we propose a novel feature-extraction strategy to obtain the robust features for training the machine-learning driven occupancy-counter. We formulate the underlying indoor occupancy-counting problem as the multi-classification problem such that the k nearest neighbor (KNN) classifier is adopted to identify the number of occupants on line. Experimental results from realworld data demonstrate that our proposed new approach leads to a promising classification-accuracy of 95.8% for indoor occupancy counting. In the comparative study based on the realworld data, our proposed novel indoor occupancy-counting method greatly outperforms other existing schemes.