Out-of-Distribution Detection for Radar-based Gesture Recognition Using Metric-Learning
Stadelmayer T, Servadei L, Santra A, Weigel R, Lurz F (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: IEEE Computer Society
Book Volume: 2023-January
Pages Range: 44-47
Conference Proceedings Title: IEEE Radio and Wireless Symposium, RWS
Event location: Las Vegas, NV, USA
ISBN: 9781665493444
DOI: 10.1109/RWS55624.2023.10046325
Abstract
The paper addresses the question how and to what extent metric learning can be beneficial for reducing the false alarm rate in radar-based hand gesture recognition systems. To this end, we evaluate different metric learning approaches for out-of-distribution or unknown motion detection. We found that metric learning can help to significantly increase the out-of-distribution capabilities of the network. We further investigated what conditions must be met for metric learning to work well, and found that the composition of the data set for known gestures has a large influence on the out-of-distribution detection rate.
Authors with CRIS profile
Thomas Stadelmayer
Lehrstuhl für Technische Elektronik (LTE)
Robert Weigel
Lehrstuhl für Technische Elektronik (LTE)
Additional Organisation(s)
Involved external institutions
Germany (DE)How to cite
APA:
Stadelmayer, T., Servadei, L., Santra, A., Weigel, R., & Lurz, F. (2023). Out-of-Distribution Detection for Radar-based Gesture Recognition Using Metric-Learning. In IEEE Radio and Wireless Symposium, RWS (pp. 44-47). Las Vegas, NV, USA: IEEE Computer Society.
MLA:
Stadelmayer, Thomas, et al. "Out-of-Distribution Detection for Radar-based Gesture Recognition Using Metric-Learning." Proceedings of the 2023 IEEE Radio and Wireless Symposium, RWS 2023, Las Vegas, NV, USA IEEE Computer Society, 2023. 44-47.
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