论文标题
自动选择：3D多对象跟踪的自动和动态检测选择
AutoSelect: Automatic and Dynamic Detection Selection for 3D Multi-Object Tracking
论文作者
论文摘要
3D多对象跟踪是机器人感知系统（例如自动驾驶车辆）中的重要组成部分。最近的工作遵循通过检测管道跟踪，该管道的目的是将过去的轨迹与当前框架中的检测匹配。为了避免与假阳性检测匹配，先前的工作通过阈值过滤了置信度较低的检测。但是，找到适当的阈值是不平凡的，这需要通过消融研究进行大量的手动搜索。同样，此阈值对许多因素（例如目标对象类别）敏感，因此，如果这些因素发生变化，我们需要重新搜索阈值。为了简化此过程，我们建议自动选择高质量检测并删除手动阈值搜索所需的工作。同样，先前的工作通常使用每个数据序列的单个阈值，该序列在特定帧或某些对象上是最佳的。相反，我们动态搜索每个帧或每个对象的阈值，以进一步提高性能。通过对Kitti和Nuscenes的实验，我们的方法可以在保持召回时过滤$ 45.7 \％$ frese阳性，从而实现了新的S.O.T.A.性能并消除对手动阈值调整的需求。
3D multi-object tracking is an important component in robotic perception systems such as self-driving vehicles. Recent work follows a tracking-by-detection pipeline, which aims to match past tracklets with detections in the current frame. To avoid matching with false positive detections, prior work filters out detections with low confidence scores via a threshold. However, finding a proper threshold is non-trivial, which requires extensive manual search via ablation study. Also, this threshold is sensitive to many factors such as target object category so we need to re-search the threshold if these factors change. To ease this process, we propose to automatically select high-quality detections and remove the efforts needed for manual threshold search. Also, prior work often uses a single threshold per data sequence, which is sub-optimal in particular frames or for certain objects. Instead, we dynamically search threshold per frame or per object to further boost performance. Through experiments on KITTI and nuScenes, our method can filter out $45.7\%$ false positives while maintaining the recall, achieving new S.O.T.A. performance and removing the need for manually threshold tuning.
