Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3709
Publisher DOI: 10.1109/ACCESS.2021.3077690
Title: A Bayesian framework for integrated deep metric learning and tracking of vulnerable road users using automotive radars
Language: English
Authors: Dubey, Anand 
Santra, Avik 
Fuchs, Jonas 
Lübke, Maximilian 
Weigel, Robert 
Lurz, Fabian 
Editor: Huang, Weimin 
Keywords: Automotive radar;Bayesian framework;deep metric learning;integrated classification-tracking;unscented Kalman filter
Issue Date: 5-May-2021
Publisher: IEEE
Source: IEEE Access 9: 9423952, 68758-68777 (2021)
Journal: IEEE access 
Abstract (english): 
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.
URI: http://hdl.handle.net/11420/10047
DOI: 10.15480/882.3709
ISSN: 2169-3536
Institute: Hochfrequenztechnik E-3 
Document Type: Article
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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