Human motion training data generation for radar based deep learning applications

peer-reviewed
Erstveröffentlichung
2018-05-09Autoren
Ishak, Karim
Appenrodt, Nils
Dickmann, Jürgen
Waldschmidt, Christian
Beitrag zu einer Konferenz
Erschienen 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
Fakultäten
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutionen
Institut für MikrowellentechnikExterne Kooperationen
Daimler AGDokumentversion
Akzeptierte VersionKonferenz
International Conference on Microwaves for Intelligent Mobility (ICMIM), 2018-04-16 - 2018-04-18, München
Zusammenfassung
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.
Originalpublikation
10.1109/ICMIM.2018.8443559Schlagwörter
[GND]: Deep learning[LCSH]: Machine learning
[Freie Schlagwörter]: Micro-Doppler | Radar data generation
[DDC Sachgruppe]: DDC 620 / Engineering & allied operations
Metadata
Zur LanganzeigeDOI & Zitiervorlage
Nutzen Sie bitte diesen Identifier für Zitate & Links: 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
Verschiedene Zitierstile >