In this paper, we use a low-cost low-power mm-wave frequency modulated continuous wave (FMCW) radar for the in-vehicle occupant detection. We propose an algorithm using Capon filter for the joint range-azimuth estimation. Then, the minimum necessary features are extracted to train machine learning classifiers to have reasonable computational complexity while achieving high accuracy. In addition, experiments were carried out in a minivan to detect occupancy of each row using support vector machine (SVM). Finally, our proposed system achieved 97.8% accuracy on average in finding the defined scenarios. Moreover, the system can correctly identify if the vehicle is occupied or not with 100% accuracy.
@article{arxiv.1908.04417,
title = {Low-cost low-power in-vehicle occupant detection with mm-wave FMCW radar},
author = {Mostafa Alizadeh and Hajar Abedi and George Shaker},
journal= {arXiv preprint arXiv:1908.04417},
year = {2020}
}