HAROOD: Human Activity Classification and Out-of-Distribution Detection with Short-Range FMCW Radar

• URL: http://arxiv.org/abs/2312.08894v1
• Date: Thu, 14 Dec 2023 12:56:28 GMT
• Title: HAROOD: Human Activity Classification and Out-of-Distribution Detection with Short-Range FMCW Radar
• Authors: Sabri Mustafa Kahya, Muhammet Sami Yavuz, Eckehard Steinbach
• Abstract summary: We propose HAROOD as a short-range FMCW radar-based human activity classifier and out-of-distribution detector. It aims to classify human sitting, standing, and walking activities and to detect any other moving or stationary object as OOD. On our dataset collected by 60 GHz short-range FMCW radar, we achieve an average classification accuracy of 96.51%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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