Data association and track-to-track association, two fundamental problems in single-sensor and multi-sensor multi-target tracking, are instances of an NP-hard combinatorial optimization problem known as the multidimensional assignment problem (MDAP). Over the last few years, data-driven approaches to tackling MDAPs in tracking have become increasingly popular. We argue that viewing multi-target tracking as an assignment problem conceptually unifies the wide variety of machine learning methods that have been proposed for data association and track-to-track association. In this survey, we review recent literature, provide rigorous formulations of the assignment problems encountered in multi-target tracking, and review classic approaches used prior to the shift towards data-driven techniques. Recent attempts at using deep learning to solve NPhard combinatorial optimization problems, including data association, are discussed as well. We highlight representation learning methods for multi-sensor applications and conclude by providing an overview of current multi-target tracking benchmarks.
We also refer to the survey by Emami et al. REF who viewed multiple object tracking as an assignment problem and unified a variety of tracking approaches under this formulation.
Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking
ArXiv
ArXiv
Computer Science