Automated Radar Data Labeling using MoveNet for Human Gesture Recognition

Publikationstyp
Conference Paper
Publikationsdatum
2023-12
Sprache
English
Author
Maiwald, Timo 
Gabsteiger, Jasmin 
Weigel, Robert 
Lurz, Fabian 
Hochfrequenztechnik E-3 
TORE-URI
https://hdl.handle.net/11420/46390
Start Page
820
End Page
822
Citation
Asia-Pacific Microwave Conference Proceedings (APMC 2023)
Contribution to Conference
Asia-Pacific Microwave Conference, APMC 2023 
Publisher DOI
10.1109/APMC57107.2023.10439908
Scopus ID
2-s2.0-85186675500
ISBN
9781665494182
Integrated radar sensors constitute an emerging technology used from smart home to autonomous driving applications. In combination with machine learning for radar data signal processing, they experience increasing popularity and are heavily investigated. These methods however need huge amounts of labeled training data to perform well in realistic scenarios, which is time consuming and cost intensive. To increase data acquisition speed, this paper presents a system using a fast computer vision model for automated radar data labeling. It is capable of generating 10 labeled radar data frames per second. To demonstrate the abilities it is used for human gesture recognition.
Schlagworte
computer vision
fmcw radar
gesture recognition
machine learning
MLE@TUHH
DDC Class
621: Applied Physics