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Optical 3D Object Recognition for Automated Driving

  • In this contribution, we propose an system setup for the detection andclassification of objects in autonomous driving applications. The recognition algo-rithm is based upon deep neural networks, operating in the 2D image domain. Theresults are combined with data of a stereo camera system to finally incorporatethe 3D object information into our mapping framework. The detection systemisIn this contribution, we propose an system setup for the detection andclassification of objects in autonomous driving applications. The recognition algo-rithm is based upon deep neural networks, operating in the 2D image domain. Theresults are combined with data of a stereo camera system to finally incorporatethe 3D object information into our mapping framework. The detection systemis locally running upon the onboard CPU of the vehicle. Several network archi-tectures are implemented and evaluated with respect to accuracy and run-timedemands for the given camera and hardware setup.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/4369
Bibliografische Angaben
Title (English):Optical 3D Object Recognition for Automated Driving
Conference:The Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020), Karlsruhe, 13th May 2020
Author:Raphael Schwarz, Marin B. Marinov, Stefan HenselStaff MemberORCiDGND
Year of Publication:2020
Creating Corporation:Hochschule Offenburg
Page Number:9
First Page:127
Last Page:135
Parent Title (English):Artificial Intelligence : Research Impact on Key Industries. Proceedings of the Upper-Rhine Artificial Intelligence Symposium
Editor:Andreas Christ, Franz Quint
ISBN:978-3-943301-29-8 (eBook)
ISBN:978-3-943301-28-1 (Print)
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Projekte / Schluckspecht
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
ArXiv Id:http://arxiv.org/abs/2010.16241