具有物体永久性的离线跟踪
Offline Tracking with Object Permanence
AI总结：
本文提出一种利用物体永久性概念的离线跟踪模型，通过Re-ID模块和轨迹补全模块恢复被遮挡物体的轨迹，显著提升3D多目标跟踪性能，可用于离线自动标注。
摘要：
为了降低人工标注自动驾驶数据集的昂贵劳动成本，一种替代方案是使用离线感知系统自动标注数据集。然而，物体可能会被暂时遮挡。数据集中的此类遮挡场景很常见，但在离线自动标注中尚未得到充分探索。在这项工作中，我们提出了一种专注于被遮挡物体轨迹的离线跟踪模型。该模型利用了物体永久性的概念，即物体即使不再被观测到也仍然存在。该模型包含三个部分：一个标准的在线跟踪器、一个在遮挡前后关联轨迹片段的重新识别（Re-ID）模块，以及一个补全碎片化轨迹的轨迹补全模块。Re-ID模块和轨迹补全模块使用矢量化地图作为输入之一，以利用遮挡信息细化跟踪结果。该模型能够有效恢复被遮挡物体的轨迹。它通过显著改善原始在线跟踪结果，在3D多目标跟踪中取得了最先进的性能，表明其有潜力作为有用的插件应用于离线自动标注中，通过恢复遮挡来改进跟踪。
英文摘要：
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 auto labeling. 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 significantly improving the original online tracking result, showing its potential to be applied in offline auto labeling as a useful plugin to improve tracking by recovering occlusions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
