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Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking
The paper considers the problem of tracking an unknown and time-varying number of unlabeled moving objects using multiple unordered measurements with unknown association to the objects. The proposed tracking approach integrates Bayesian nonparametric modeling with Markov chain Monte Carlo methods to...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749759/ https://www.ncbi.nlm.nih.gov/pubmed/35009928 http://dx.doi.org/10.3390/s22010388 |
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author | Moraffah, Bahman Papandreou-Suppappola, Antonia |
author_facet | Moraffah, Bahman Papandreou-Suppappola, Antonia |
author_sort | Moraffah, Bahman |
collection | PubMed |
description | The paper considers the problem of tracking an unknown and time-varying number of unlabeled moving objects using multiple unordered measurements with unknown association to the objects. The proposed tracking approach integrates Bayesian nonparametric modeling with Markov chain Monte Carlo methods to estimate the parameters of each object when present in the tracking scene. In particular, we adopt the dependent Dirichlet process (DDP) to learn the multiple object state prior by exploiting inherent dynamic dependencies in the state transition using the dynamic clustering property of the DDP. Using the DDP to draw the mixing measures, Dirichlet process mixtures are used to learn and assign each measurement to its associated object identity. The Bayesian posterior to estimate the target trajectories is efficiently implemented using a Gibbs sampler inference scheme. A second tracking approach is proposed that replaces the DDP with the dependent Pitman–Yor process in order to allow for a higher flexibility in clustering. The improved tracking performance of the new approaches is demonstrated by comparison to the generalized labeled multi-Bernoulli filter. |
format | Online Article Text |
id | pubmed-8749759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87497592022-01-12 Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking Moraffah, Bahman Papandreou-Suppappola, Antonia Sensors (Basel) Article The paper considers the problem of tracking an unknown and time-varying number of unlabeled moving objects using multiple unordered measurements with unknown association to the objects. The proposed tracking approach integrates Bayesian nonparametric modeling with Markov chain Monte Carlo methods to estimate the parameters of each object when present in the tracking scene. In particular, we adopt the dependent Dirichlet process (DDP) to learn the multiple object state prior by exploiting inherent dynamic dependencies in the state transition using the dynamic clustering property of the DDP. Using the DDP to draw the mixing measures, Dirichlet process mixtures are used to learn and assign each measurement to its associated object identity. The Bayesian posterior to estimate the target trajectories is efficiently implemented using a Gibbs sampler inference scheme. A second tracking approach is proposed that replaces the DDP with the dependent Pitman–Yor process in order to allow for a higher flexibility in clustering. The improved tracking performance of the new approaches is demonstrated by comparison to the generalized labeled multi-Bernoulli filter. MDPI 2022-01-05 /pmc/articles/PMC8749759/ /pubmed/35009928 http://dx.doi.org/10.3390/s22010388 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Moraffah, Bahman Papandreou-Suppappola, Antonia Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking |
title | Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking |
title_full | Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking |
title_fullStr | Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking |
title_full_unstemmed | Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking |
title_short | Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking |
title_sort | bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749759/ https://www.ncbi.nlm.nih.gov/pubmed/35009928 http://dx.doi.org/10.3390/s22010388 |
work_keys_str_mv | AT moraffahbahman bayesiannonparametricmodelingforpredictingdynamicdependenciesinmultipleobjecttracking AT papandreousuppappolaantonia bayesiannonparametricmodelingforpredictingdynamicdependenciesinmultipleobjecttracking |