# Multi-Sensor Multi-object Tracking with the Generalized Labeled Multi-Bernoulli Filter

## TL;DR

This paper introduces an efficient multi-sensor multi-object tracking algorithm using the generalized labeled multi-Bernoulli filter, optimized with Gibbs sampling for improved computational performance.

## Contribution

It presents a novel implementation of the GLMB filter that combines joint prediction and update with Gibbs sampling-based truncation, reducing computational complexity.

## Key findings

- Quadratic complexity in the number of objects
- Linear complexity in the number of measurements per sensor
- Efficient multi-sensor multi-object tracking performance

## Abstract

This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has quadratic complexity in the number of hypothesized object and linear in the number of measurements of each individual sensors.