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Human motion training data generation for radar based deep learning applications

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peer-reviewed

Erstveröffentlichung
2018-05-09
Authors
Ishak, Karim
Appenrodt, Nils
Dickmann, Jürgen
Waldschmidt, Christian
Beitrag zu einer Konferenz


Published in
2018 International Conference on Microwaves for Intelligent Mobility (ICMIM) / Institute of Electrical and Electronics Engineers (Hrsg.). - : IEEE, 2018. - eISSN 978-1-5386-1725-0
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Institutions
Institut für Mikrowellentechnik
External cooperations
Daimler AG
Document version
accepted version
Conference
International Conference on Microwaves for Intelligent Mobility (ICMIM), 2018-04-16 - 2018-04-18, München
Abstract
Radar sensors are utilized for detection and classification purposes in various applications. In order to use deep learning techniques, lots of training data are required. Accordingly, lots of measurements and labelling tasks are then needed. For the purpose of pre-training or examining first ideas before bringing them into reality, synthetic radar data are of great help. In this paper, a workflow for automatically generating radar data of human gestures is presented, starting with creating the desired animations until synthesizing radar data and getting the final required dataset. The dataset could then be used for training deep learning models. A classification scenario applying this workflow is also introduced.
Original publication
10.1109/ICMIM.2018.8443559
Subject headings
[GND]: Deep learning
[LCSH]: Machine learning
[Free subject headings]: Micro-Doppler | Radar data generation
[DDC subject group]: DDC 620 / Engineering & allied operations
License
Standard
https://oparu.uni-ulm.de/xmlui/license_v3

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DOI & citation

Please use this identifier to cite or link to this item: http://dx.doi.org/10.18725/OPARU-6512

Ishak, Karim et al. (2018): Human motion training data generation for radar based deep learning applications. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. http://dx.doi.org/10.18725/OPARU-6512
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