@conference {21053,
	title = {Low-cost low-power in-vehicle occupant detection with mm-wave FMCW radar},
	booktitle = {IEEE Sensor 2019},
	year = {2019},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {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.<br>\&nbsp;},
	author = {Mostafa Alizadeh and Hajar Abedi and George Shaker}
}
