A Bayesian framework for integrated deep metric learning and tracking of vulnerable road users using automotive radars

Medientyp: E-Artikel

Titel: A Bayesian framework for integrated deep metric learning and tracking of vulnerable road users using automotive radars

Beteiligte: Dubey, Anand [VerfasserIn]; Santra, Avik [VerfasserIn]; Fuchs, Jonas [VerfasserIn]; Lübke, Maximilian [VerfasserIn]; Weigel, Robert [VerfasserIn]; Lurz, Fabian [VerfasserIn]; Huang, Weimin [HerausgeberIn]

Körperschaft: Technische Universität Hamburg ; Technische Universität Hamburg, Institut für Hochfrequenztechnik

Erschienen: 2021

Sprache: Englisch

DOI: 10.15480/882.3709; 10.1109/ACCESS.2021.3077690

ISSN: 2169-3536

Identifikator:

Schlagwörter: Automotive radar ; Bayesian framework ; deep metric learning ; integrated classification-tracking ; unscented Kalman filter

Entstehung:

Anmerkungen: Sonstige Körperschaft: Technische Universität Hamburg
Sonstige Körperschaft: Technische Universität Hamburg, Institut für Hochfrequenztechnik

Beschreibung: With the recent advancements in radar systems, radar sensors offer a promising and effective perception of the surrounding. This includes target detection, classification and tracking. Compared to the state-of-the-art, where the state vector of classical tracker considers only localization parameters, this paper proposes an integrated Bayesian framework by augmenting state vector with feature embedding as appearance parameter together with localization parameter. In context of automotive vulnerable road users (VRUs) such as pedestrian and cyclist, the classical tracker poses multiple challenges to preserve the identity of the tracked target during partial or complete occlusion, due to low inter-class (pedestrian-cyclist) variations and strong similarity between intra-class (pedestrian-pedestrian). Subsequently, feature embedding corresponding to target's micro-Doppler signature are learned using novel Bayesian based deep metric learning approaches. The tracker's performance is optimized due to a better separability of the targets. At the same time, the classifiers' performance is enhanced due to Bayesian formulation utilizing the temporal smoothing of the classifier's embedding vector. In this work, we demonstrate the performance of the proposed Bayesian framework using several vulnerable user targets based on a 77 GHz automotive radar.

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