We propose HAROOD as a short-range FMCW radar-based human activity classifier and out-of-distribution (OOD) detector. It aims to classify human sitting, standing, and walking activities and to detect any other moving or stationary object as OOD. We introduce a two-stage network. The first stage is trained with a novel loss function that includes intermediate reconstruction loss, intermediate contrastive loss, and triplet loss. The second stage uses the first stage's output as its input and is trained with cross-entropy loss. It creates a simple classifier that performs the activity classification. On our dataset collected by 60 GHz short-range FMCW radar, we achieve an average classification accuracy of 96.51%. Also, we achieve an average AUROC of 95.04% as an OOD detector. Additionally, our extensive evaluations demonstrate the superiority of HAROOD over the state-of-the-art OOD detection methods in terms of standard OOD detection metrics.
@article{arxiv.2312.08894,
title = {HAROOD: Human Activity Classification and Out-of-Distribution Detection with Short-Range FMCW Radar},
author = {Sabri Mustafa Kahya and Muhammet Sami Yavuz and Eckehard Steinbach},
journal= {arXiv preprint arXiv:2312.08894},
year = {2023}
}