On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking

Anthony Trezza, Donald J. Bucci, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently truncate the single-sensor and multi-sensor δ-generalized labeled multi-Bernoulli posterior density as well as the multi-sensor adaptive labeled multi-Bernoulli birth distribution. However, only a limited discussion has been provided regarding key Gibbs sampler architecture details including the Markov chain Monte Carlo sample generation technique and early termination criteria. This paper begins with a brief background on Markov chain Monte Carlo methods and a review of the Gibbs sampler implementations proposed for labeled random finite sets filters. Next, we propose a short chain, multi-simulation sample generation technique that is well suited for these applications and enables a parallel processing implementation. Additionally, we present two heuristic early termination criteria that achieve similar sampling performance with substantially fewer Markov chain observations. Finally, the benefits of the proposed Gibbs samplers are demonstrated via two Monte Carlo simulations.

Original languageEnglish (US)
Title of host publication2023 26th International Conference on Information Fusion, FUSION 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798890344854
DOIs
StatePublished - 2023
Externally publishedYes
Event26th International Conference on Information Fusion, FUSION 2023 - Charleston, United States
Duration: Jun 27 2023Jun 30 2023

Publication series

Name2023 26th International Conference on Information Fusion, FUSION 2023

Conference

Conference26th International Conference on Information Fusion, FUSION 2023
Country/TerritoryUnited States
CityCharleston
Period6/27/236/30/23

Keywords

  • Gibbs Sampling
  • Markov Chain Monte Carlo
  • Multi-object Tracking
  • Random Finite Sets

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Instrumentation

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