Nonparametric Bayesian Methods and the Dependent Pitman-Yor Process for Modeling Evolution in Multiple Object Tracking

Bahman Moraffah, Antonia Papandreou-Suppappola, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we propose a family of dependent Pitman-Yor (DPY) processes to model the state prior for multiple object tracking. This process is shown to be more flexible and a better match than the dependent Dirichlet process in tracking a time-varying number of objects. The DPY model directly incorporates learning multiple parameters from correlated information. Integrated with a Dirichlet process mixture model, the overall approach estimates time dependent object cardinality, provides object labeling, and identifies object associated measurements. Using Markov chain Monte Carlo sampling methods, the performance of the DPY based approach is demonstrated and compared to the labeled multi Bernoulli tracker.

Original languageEnglish (US)
Title of host publicationFUSION 2019 - 22nd International Conference on Information Fusion
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780996452786
StatePublished - Jul 2019
Event22nd International Conference on Information Fusion, FUSION 2019 - Ottawa, Canada
Duration: Jul 2 2019Jul 5 2019

Publication series

NameFUSION 2019 - 22nd International Conference on Information Fusion

Conference

Conference22nd International Conference on Information Fusion, FUSION 2019
Country/TerritoryCanada
CityOttawa
Period7/2/197/5/19

Keywords

  • dependent Pitman-Yor process
  • Dirichlet process mixture model
  • Markov chain Monte Carlo sampling
  • multiple object tracking
  • nonparametric Bayesian method

ASJC Scopus subject areas

  • Information Systems
  • Instrumentation

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