A Merge/Split Algorithm for Multitarget Tracking Using Generalized Labeled Multi-Bernoulli Filters

TL;DR

This paper introduces a novel Merge/Split algorithm for dGLMB filters in multitarget tracking, enabling adaptive resolution of tracking hypotheses to improve computational efficiency and track maintenance.

Contribution

The paper presents a new splitting algorithm based on a marginalization scheme that allows adaptive factorization of the filtering density in dGLMB filters.

Findings

The algorithm effectively prevents track degeneracy.

Adaptive resolution improves computational efficiency.

The method maintains tracking accuracy with fewer hypotheses.

Abstract

The class of Labeled Random Finite Set filters known as the delta-Generalized Labeled Multi-Bernoulli (dGLMB) filter represents the filtering density as a set of weighted hypotheses, with each hypothesis consisting of a set of labeled tracks, which are in turn pairs of a track label and a track kinematic density. Upon update with a batch of measurements, each hypothesis gives rise to many child hypotheses, and therefore truncation has to be performed for any practical application. Finite compute budget can lead to degeneracy that drops tracks. To mitigate, we adopt a factored filtering density through the use of a novel Merge/Split algorithm. Merging has long been established in the literature; our splitting algorithm is enabled by an efficient and effective marginalization scheme, through indexing a kinematic density by the measurement IDs (in a moving window) that have been used in its update. This allows us to determine when independence can be considered to hold approximately for a given tolerance, so that the "resolution" of tracking is adaptively chosen, from a single factor (dGLMB), to all-singleton factors (Labeled Multi-Bernoulli, LMB), and anywhere in between.