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AuthorIshak, Karimdc.contributor.author
AuthorAppenrodt, Nilsdc.contributor.author
AuthorDickmann, Jürgendc.contributor.author
AuthorWaldschmidt, Christiandc.contributor.author
Date of accession2018-05-09T10:02:30Zdc.date.accessioned
Available in OPARU since2018-05-09T10:02:30Zdc.date.available
Date of first publication2018-05-09dc.date.issued
AbstractRadar 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.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
ID of original publ.10.1109/ICMIM.2018.8443559dc.relation.uri
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordMicro-Dopplerdc.subject
KeywordRadar data generationdc.subject
Dewey Decimal GroupDDC 620 / Engineering & allied operationsdc.subject.ddc
LCSHMachine learningdc.subject.lcsh
TitleHuman motion training data generation for radar based deep learning applicationsdc.title
Resource typeBeitrag zu einer Konferenzdc.type
VersionacceptedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-6512dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-6569-9dc.identifier.urn
GNDDeep learningdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Mikrowellentechnikuulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
In cooperation withDaimler AGuulm.cooperation
Source - Title of source2018 International Conference on Microwaves for Intelligent Mobility (ICMIM)source.title
Quellenangabe - HerausgeberInstitute of Electrical and Electronics Engineerssource.contributor.editor1
Source - Place of publicationIEEEsource.publisher
Source - Year2018source.year
Source - eISSN978-1-5386-1725-0source.identifier.eissn
Conference nameInternational Conference on Microwaves for Intelligent Mobility (ICMIM)uulm.conferenceName
Conference placeMünchenuulm.conferencePlace
Conference start date2018-04-16uulm.conferenceStartDate
Conference end date2018-04-18uulm.conferenceEndDate


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