A Human Pose Recognition Model Comparison to Label Radar Data for Gesture Recognition in Robotics
Maiwald T, Ederer J, Fischer G, Lurz F (2025)
Publication Status: Accepted
Publication Type: Unpublished / Preprint
Future Publication Type: Conference contribution
Publication year: 2025
Abstract
Instantaneous radar data labeling during data set
acquisition, through camera based computer vision methods, is a
challenging task. In context of human pose and gesture recognition,
there is a huge amount of computer vision models available and
whether they are suitable for instantaneous data labeling needs
to be investigated. Therefore, this paper compares 20 different
models regarding inference time and human keypoint location
deviations. The most suitable model is statistically investigated
further using more than 250 thousand images, where person
presence detection and gesture detection are evaluated using
simple threshold methods. Obtained results reveal 94% of all
images are correctly classified.
Authors with CRIS profile
Timo Maiwald
Lehrstuhl für Intelligente Technische Elektronik und Systeme
Georg Fischer
Professur für Technische Elektronik
Involved external institutions
Germany (DE)How to cite
APA:
Maiwald, T., Ederer, J., Fischer, G., & Lurz, F. (2025). A Human Pose Recognition Model Comparison to Label Radar Data for Gesture Recognition in Robotics. (Unpublished, Accepted).
MLA:
Maiwald, Timo, et al. A Human Pose Recognition Model Comparison to Label Radar Data for Gesture Recognition in Robotics. Unpublished, Accepted. 2025.
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