(video) Co-Development of Automatic Annotation for ML and Sensor Fusion Improvement System Stefan Haag

3rd 3D-DLAD workshop : https://sites.google.com/view/3d-dlad-v3-iv2021/schedule
Abstract : Labeled radar datasets are crucial for establishing artificial intelligence-based methods on radar data for environment perception.

In particular, fast and efficient acquisition of new data under a specific sensor setup is essential. We present a framework that utilizes Bayesian-based approaches and eventually fusion methods to provide veritable and precise object trajectories and shape estimation to provide annotation labels on the detection level under various supervision levels.

Simultaneously, the framework provides continuous evaluation of tracking performance and label annotation through automated feedback evaluation. 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 allows the integration of information from additional sensors for improved results, but it allows the execution as a radar-only application.