This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT)
framework. It emphasizes spatio-temporal continuity and integrates both past
and future reasoning for tracked objects. Thus, we name it "Past-and-Future
reasoning for Tracking" (PF-Track). Specifically, our method adapts the
"tracking by attention" framework and represents tracked instances coherently
over time with object queries. To explicitly use historical cues, our "Past
Reasoning" module learns to refine the tracks and enhance the object features
by cross-attending to queries from previous frames and other objects. The
"Future Reasoning" module digests historical information and predicts robust
future trajectories. In the case of long-term occlusions, our method maintains
the object positions and enables re-association by integrating motion
predictions. On the nuScenes dataset, our method improves AMOTA by a large
margin and remarkably reduces ID-Switches by 90% compared to prior approaches,
which is an order of magnitude less. The code and models are made available at
this https URL.

本文介绍了一种名为 PF-Track 的多相机 3D 多目标跟踪框架，它重点强调时空连续性，过去和未来的推理，采用 “注意跟踪” 框架，并使用对象查询一致地表示跟踪实例，以明确使用历史线索，显式地引用前面帧和其他对象的查询，从而学习优化轨迹并增强物体特征。