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 Language: English

Publication Status: Accepted

Publication Type: Conference contribution

Future Publication Type: Conference contribution

Publication year: 2025

Event location: San Juan

DOI: 10.1109/LAMC63321.2025.10880548

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

Related research project(s)

Embedded Machine Learning (eMiL) Oct. 1, 2021 - Sept. 30, 2024

Involved external institutions

Otto-von-Guericke-Universität Magdeburg DE 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. In Proceedings of the Latin America Microwave Conference (LAMC). San Juan.

MLA:

Maiwald, Timo, et al. "A Human Pose Recognition Model Comparison to Label Radar Data for Gesture Recognition in Robotics." Proceedings of the Latin America Microwave Conference (LAMC), San Juan 2025.

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