Image-Based Pedestrian Classification for Automotive Radar

Prophet R, Hoffmann M, Ossowska A, Malik W, Sturm C, Vossiek M (2018)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2018

Event location: Madrid ES

DOI: 10.23919/EuRAD.2018.8546611

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.

Authors with CRIS profile

Robert Prophet Lehrstuhl für Hochfrequenztechnik (LHFT) Marcel Hoffmann Lehrstuhl für Hochfrequenztechnik (LHFT) Martin Vossiek Lehrstuhl für Hochfrequenztechnik (LHFT)

How to cite

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.

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