LAUSR

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 3-D 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 multiclassification problem such that the ${k}$ nearest-neighbor (KNN) classifier is adopted to identify the number of occupants on line. Experimental results from real-world 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 real-world data, our proposed novel indoor occupancy counting method greatly outperforms other existing schemes.