Multiple target tracking based on sets of trajectories

TL;DR

This paper introduces a Bayesian framework for multiple target tracking that models sets of trajectories using random finite sets, providing a comprehensive probabilistic description of target movements.

Contribution

It presents a novel Bayesian approach based on sets of trajectories and develops conjugate multitrajectory density functions for standard tracking models.

Findings

Provides a full Bayesian characterization of trajectories

Develops conjugate multitrajectory density functions

Enhances understanding of multi-object tracking probabilistics

Abstract

We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to MTT should characterise the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multi-object density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions.