Learning data association without data association: An EM approach to neural assignment prediction

• URL: http://arxiv.org/abs/2105.00369v1
• Date: Sun, 2 May 2021 01:11:09 GMT
• Title: Learning data association without data association: An EM approach to neural assignment prediction
• Authors: Michael Burke, Subramanian Ramamoorthy
• Abstract summary: This paper introduces an expectation maximisation approach to train neural models for data association. It does not require labelling information to train a model for object recognition. Importantly, networks trained using the proposed approach can be re-used in downstream tracking applications.
• Score: 12.970250708769708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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