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A Bayesian Framework for Integrated Deep Metric Learning and Tracking of Vulnerable Road Users Using Automotive Radars
2021
IEEE Access
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-theart, 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
doi:10.1109/access.2021.3077690
fatcat:6hbklgq6s5fn5me6utfu7sh4yi