To reduce the expensive labor cost for manual labeling autonomous driving
datasets, an alternative is to automatically label the datasets using an
offline perception system. However, objects might be temporally occluded. Such
occlusion scenarios in the datasets are common yet underexplored in offline
autolabeling. In this work, we propose an offline tracking model that focuses
on occluded object tracks. It leverages the concept of object permanence which
means objects continue to exist even if they are not observed anymore. The
model contains three parts: a standard online tracker, a re-identification
(Re-ID) module that associates tracklets before and after occlusion, and a
track completion module that completes the fragmented tracks. The Re-ID module
and the track completion module use the vectorized map as one of the inputs to
refine the tracking results with occlusion. The model can effectively recover
the occluded object trajectories. It achieves state-of-the-art performance in
3D multi-object tracking by improving over the original online tracking result
by 45% IDS and 2% AMOTA on the vehicle tracks.

通过利用物体的持续存在性概念，我们提出了一个离线追踪模型，专注于被遮挡的物体轨迹，并使用向量化地图来改善跟踪结果，从而有效地恢复被遮挡的物体轨迹。在车辆轨迹方面，该模型在 3D 多物体跟踪中取得了显著的改善，比原始在线跟踪结果提高了 45% 的 IDS 和 2% 的 AMOTA。