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  4. Out-of-distribution detection for radar-based gesture recognition using metric-learning
 
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Out-of-distribution detection for radar-based gesture recognition using metric-learning

Publikationstyp
Conference Paper
Date Issued
2023-01
Sprache
English
Author(s)
Stadelmayer, Thomas  
Servadei, Lorenzo  
Santra, Avik  
Weigel, Robert  
Lurz, Fabian  
Institut
Hochfrequenztechnik E-3  
TORE-URI
http://hdl.handle.net/11420/15035
Start Page
44
End Page
47
Citation
IEEE Radio and Wireless Symposium (RWS 2023)
Contribution to Conference
IEEE Radio and Wireless Symposium, RWS 2023  
Publisher DOI
10.1109/RWS55624.2023.10046325
Scopus ID
2-s2.0-85149264353
Publisher
IEEE
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.
Subjects
FMCW radar
hand gesture recognition
metric learning
out-of-distribution detection
MLE@TUHH
DDC Class
600: Technik
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