# Multiple Object Tracking in Unknown Backgrounds with Labeled Random Finite Sets

## TL;DR

This paper introduces an adaptive online multiple object tracking algorithm that leverages the GLMB filter to learn background clutter and detection parameters in real-time, improving tracking accuracy in unknown environments.

## Contribution

It demonstrates how the GLMB filter can be tailored to adaptively learn background parameters during tracking, a novel approach for unknown and varying backgrounds.

## Key findings

- Enhanced tracking performance in unknown backgrounds
- Real-time learning of clutter and detection parameters
- Proven Bayes optimality of the adapted GLMB filter

## Abstract

This paper proposes an on-line multiple object tracking algorithm that can operate in unknown background. In a majority of multiple object tracking applications, model parameters for background processes such as clutter and detection are unknown and vary with time, hence the ability of the algorithm to adaptively learn the these parameters is essential in practice. In this work, we detail how the Generalized Labeled Multi Bernouli (GLMB) filter a tractable and provably Bayes optimal multi-object tracker can be tailored to learn clutter and detection parameters on the fly while tracking. Provided that these background model parameters do not fluctuate rapidly compared to the data rate, the proposed algorithm can adapt to the unknown background yielding better tracking performance.