Image-Based Pedestrian Classification for Automotive Radar

Beitrag bei einer Tagung


Details zur Publikation

Autor(en): Prophet R, Hoffmann M, Ossowska A, Malik W, Sturm C, Vossiek M
Jahr der Veröffentlichung: 2018
Sprache: Englisch


Abstract

Radar sensors have become an integral part of advanced driver assistance
systems. Merely detecting targets will not, however, advance their
contribution. Rather, an object classification capability is required to
distinguish vulnerable road users from other objects, such as vehicles.
In this paper, we present a novel pedestrian classification procedure,
which uses image features from the range-Doppler-Matrix created by a 79
GHz chirp sequence radar. Experiments show single measurement success
rates of 88% for a bandwidth of 1.6 GHz. Moreover, the robustness of the
classification process is consolidated with a tracking algorithm.
Implemented in vehicles, this can be a major contribution to protect
vulnerable road users.


FAU-Autoren / FAU-Herausgeber

Hoffmann, Marcel
Lehrstuhl für Hochfrequenztechnik
Prophet, Robert
Lehrstuhl für Hochfrequenztechnik
Vossiek, Martin Prof. Dr.-Ing.
Lehrstuhl für Hochfrequenztechnik


Zitierweisen

APA:
Prophet, R., Hoffmann, M., Ossowska, A., Malik, W., Sturm, C., & Vossiek, M. (2018). Image-Based Pedestrian Classification for Automotive Radar. In Proceedings of the 15th European Radar Conference (EuRAD 2018). Madrid, ES.

MLA:
Prophet, Robert, et al. "Image-Based Pedestrian Classification for Automotive Radar." Proceedings of the 15th European Radar Conference (EuRAD 2018), Madrid 2018.

BibTeX: 

Zuletzt aktualisiert 2019-17-04 um 22:23