Offline Auto Labeling: BAAS

• URL: http://arxiv.org/abs/2508.09585v1
• Date: Wed, 13 Aug 2025 07:58:59 GMT
• Title: Offline Auto Labeling: BAAS
• Authors: Stefan Haag, Bharanidhar Duraisamy, Felix Govaers, Wolfgang Koch, Martin Fritzsche, Juergen Dickmann,
• Abstract summary: BAAS is a new Extended Object Tracking (EOT) and fusion-based label annotation framework for radar detections in autonomous driving.<n>Our framework utilizes Bayesian-based tracking, smoothing and eventually fusion methods to provide veritable and precise object trajectories.
• Score: 0.1979158763744267
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces BAAS, a new Extended Object Tracking (EOT) and fusion-based label annotation framework for radar detections in autonomous driving. Our framework utilizes Bayesian-based tracking, smoothing and eventually fusion methods to provide veritable and precise object trajectories along with shape estimation to provide annotation labels on the detection level under various supervision levels. Simultaneously, the framework provides evaluation of tracking performance and label annotation. If manually labeled data is available, each processing module can be analyzed independently or combined with other modules to enable closed-loop continuous improvements. The framework performance is evaluated in a challenging urban real-world scenario in terms of tracking performance and the label annotation errors. We demonstrate the functionality of the proposed approach for varying dynamic objects and class types

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