Data association is a key step within the multi-object tracking pipeline that
is notoriously challenging due to its combinatorial nature. A popular and
general way to formulate data association is as the NP-hard multidimensional
assignment problem (MDAP). Over the last few years, data-driven approaches to
assignment have become increasingly prevalent as these techniques have started
to mature. We focus this survey solely on learning algorithms for the
assignment step of multi-object tracking, and we attempt to unify various
methods by highlighting their connections to linear assignment as well as to
the MDAP. First, we review probabilistic and end-to-end optimization approaches
to data association, followed by methods that learn association affinities from
data. We then compare the performance of the methods presented in this survey,
and conclude by discussing future research directions.

本文综述了多目标跟踪管道中数据关联的问题，特别是针对学习方法在关联步骤中的应用。回顾了采用概率和端到端优化方法的数据关联方法，接着介绍了从数据中学习关联的方法，并比较了这些方法的性能。最后，讨论了未来的研究方向。