Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories

TL;DR

This paper introduces a Poisson multi-Bernoulli mixture filter designed for tracking multiple spawning targets by modeling sets of tree trajectories, significantly enhancing tracking accuracy in complex scenarios.

Contribution

It develops a novel PMBM filter on tree trajectories, incorporating spawning processes and an efficient approximation method for improved multi-target tracking.

Findings

Enhanced tracking accuracy over existing algorithms

Effective modeling of spawning targets with tree trajectories

Computational efficiency achieved through approximation

Abstract

This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter on the space of sets of tree trajectories for multiple target tracking with spawning targets. A tree trajectory contains all trajectory information of a target and its descendants, which appear due to the spawning process. Each tree contains a set of branches, where each branch has trajectory information of a target or one of the descendants and its genealogy. For the standard dynamic and measurement models with multi-Bernoulli spawning, the posterior is a PMBM density, with each Bernoulli having information on a potential tree trajectory. To enable a computationally efficient implementation, we derive an approximate PMBM filter in which each Bernoulli tree trajectory has multi-Bernoulli branches, obtained by minimising the Kullback-Leibler divergence. The resulting filter improves tracking performance of state-of-the-art algorithms in a simulated scenario.