  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.
