Semi-supervised attention-augmented convolutional autoencoder for radar-based human activity recognition

Christopher Campbell; Fauzia Ahmad

doi:10.1117/12.2622366

27 May 2022 Semi-supervised attention-augmented convolutional autoencoder for radar-based human activity recognition

Christopher Campbell, Fauzia Ahmad

Proceedings Volume 12108, Radar Sensor Technology XXVI; 121080B (2022) https://doi.org/10.1117/12.2622366
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In this paper, we consider semi-supervised training of an attention-augmented convolutional autoencoder (AACAE) for human activity recognition using radar micro-Doppler signatures. The AA-CAE learns global information in addition to spatially localized features, thus enabling the classifier to overcome the limited receptive field of a conventional convolutional autoencoder (CAE). The design also permits the possibility of semi-supervised training of the AA-CAE using training data comprising unlabeled and labeled sets. More specifically, the semisupervised training regime is implemented by first pre-training the AA-CAE via unsupervised training of the attention-augmented autoencoder with the unlabeled portion of the training data. This is followed by fine-tuning of the AA-CAE for classification using the labeled portion. Using real-data measurements of six different human activities, we demonstrate that the semi-supervised AA-CAE yields higher classification accuracy with much less labeled data than a fully-supervised conventional CAE.

Citation Download Citation

Christopher Campbell and Fauzia Ahmad "Semi-supervised attention-augmented convolutional autoencoder for radar-based human activity recognition", Proc. SPIE 12108, Radar Sensor Technology XXVI, 121080B (27 May 2022); https://doi.org/10.1117/12.2622366

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