US 12,307,821 B2
Radar-based gesture classification using a variational auto-encoder neural network
Avik Santra, Munich (DE); Souvik Hazra, Munich (DE); and Thomas Reinhold Stadelmayer, Wenzenbach (DE)
Assigned to Infineon Technologies AG, Neubiberg (DE)
Filed by Infineon Technologies AG, Neubiberg (DE)
Filed on Aug. 11, 2022, as Appl. No. 17/886,264.
Claims priority of application No. 21190926 (EP), filed on Aug. 12, 2021.
Prior Publication US 2023/0068523 A1, Mar. 2, 2023
Int. Cl. G06V 10/00 (2022.01); G06V 10/74 (2022.01); G06V 10/762 (2022.01); G06V 10/82 (2022.01); G06V 40/20 (2022.01)
CPC G06V 40/20 (2022.01) [G06V 10/761 (2022.01); G06V 10/763 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining one or more positional time spectrograms of a radar measurement of a scene comprising an object; and
based on the one or more positional time spectrograms and based on a feature embedding of a variational auto-encoder neural network, predicting a gesture class of a gesture performed by the object, wherein the gesture class is predicted based on a comparison of a mean of a distribution of the feature embedding of the variational auto-encoder neural network with one or more regions predefined in a feature space of the feature embedding.