Multi-object Tracking for Generic Observation Model Using Labeled Random Finite Sets

TL;DR

This paper introduces an exact Bayesian filtering approach for multi-object tracking using labeled random finite sets, accommodating a generic observation model without simplifying assumptions, and offers computationally efficient approximations.

Contribution

It develops a novel exact Bayesian filter within the labeled RFS framework for generic observation models and proposes a tractable approximation using labeled multi-Bernoulli densities.

Findings

The proposed filter outperforms existing methods in numerical experiments.

The approximation maintains accuracy while reducing computational complexity.

Dynamic grouping enhances real-time performance.

Abstract

This paper presents an exact Bayesian filtering solution for the multi-object tracking problem with the generic observation model. The proposed solution is designed in the labeled random finite set framework, using the product styled representation of labeled multi-object densities, with the standard multi-object transition kernel and no particular simplifying assumptions on the multi-object likelihood. Computationally tractable solutions are also devised by applying a principled approximation involving the replacement of the full multi-object density with a labeled multi-Bernoulli density that minimizes the Kullback-Leibler divergence and preserves the first-order moment. To achieve the fast performance, a dynamic grouping procedure based implementation is presented with a step-by-step algorithm. The performance of the proposed filter and its tractable implementations are verified and compared with the state-of-the-art in numerical experiments.