基于贝叶斯融合的雷达目标追踪与自动标注框架
Offline Auto Labeling: BAAS
- 采用贝叶斯追踪与平滑融合方法生成精准轨迹与形状估计
- 在真实城市场景中实现高精度检测级标注，误差显著降低
- 支持多监督层级，适合自动驾驶数据标注与持续优化
本文提出BAAS，一种用于自动驾驶雷达检测的扩展目标追踪（EOT）与融合式标注框架。该框架利用基于贝叶斯的追踪、平滑与融合方法，在不同监督水平下提供真实且精确的目标轨迹与形状估计，实现检测级别的标注标签。同时，框架可评估追踪性能与标注误差。若有手工标注数据，各处理模块可独立分析或组合使用，支持闭环持续改进。在复杂城市真实场景中，通过追踪性能与标注误差评估验证了方法的有效性，适用于多种动态目标与类别类型。
原文摘要 · Abstract (English)
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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