Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of
self-driving cars. Tracking objects, however, remains a highly challenging
problem, especially in cluttered autonomous driving scenes in which objects
tend to interact with each other in complex ways and frequently get occluded.
We propose a novel approach to MOT that uses attention to compute track
embeddings that encode the spatiotemporal dependencies between observed
objects. This attention measurement encoding allows our model to relax hard
data associations, which may lead to unrecoverable errors. Instead, our model
aggregates information from all object detections via soft data associations.
The resulting latent space representation allows our model to learn to reason
about occlusions in a holistic data-driven way and maintain track estimates for
objects even when they are occluded. Our experimental results on the Waymo
OpenDataset suggest that our approach leverages modern large-scale datasets and
performs favorably compared to the state of the art in visual multi-object
tracking.

提出一种应用注意力度量编码来解决自动驾驶场景下多目标跟踪问题的方法，此方法通过软性数据关联来聚合所有物体检测的信息，从而使得模型能够在数据驱动的方式下推理出目标物体的遮挡情况并进行跟踪，实验结果表明该方法在规模较大的数据集上优于现有的视觉多目标跟踪技术。