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BAAS: Bayesian Tracking and Fusion Assisted Object Annotation of Radar Sensor Data for Artificial Intelligence Application

: Haag, S.; Duraisamy, B.; Govaers, F.; Koch, W.; Fritzsche, M.; Dickmann, J.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Radar Conference, RadarConf 2020 : September 21-25, 2020, Florence, Italy, virtual event
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-8943-7
ISBN: 978-1-7281-8942-0
Radar Conference (RadarConf) <2020, Online>
Fraunhofer FKIE ()

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.