  Data association is a fundamental component of effective multi-object
tracking. Current approaches to data-association tend to frame this as an
assignment problem relying on gating and distance-based cost matrices, or
offset the challenge of data association to a problem of tracking by detection.
The latter is typically formulated as a supervised learning problem, and
requires labelling information about tracked object identities to train a model
for object recognition. This paper introduces an expectation maximisation
approach to train neural models for data association, which does not require
labelling information. Here, a Sinkhorn network is trained to predict
assignment matrices that maximise the marginal likelihood of trajectory
observations. Importantly, networks trained using the proposed approach can be
re-used in downstream tracking applications.
