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GLMB Tracker with Partial Smoothing

In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we...

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Detalles Bibliográficos
Autores principales: Nguyen, Tran Thien Dat, Kim, Du Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832553/
https://www.ncbi.nlm.nih.gov/pubmed/31614812
http://dx.doi.org/10.3390/s19204419
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author Nguyen, Tran Thien Dat
Kim, Du Yong
author_facet Nguyen, Tran Thien Dat
Kim, Du Yong
author_sort Nguyen, Tran Thien Dat
collection PubMed
description In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters.
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spelling pubmed-68325532019-11-25 GLMB Tracker with Partial Smoothing Nguyen, Tran Thien Dat Kim, Du Yong Sensors (Basel) Article In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters. MDPI 2019-10-12 /pmc/articles/PMC6832553/ /pubmed/31614812 http://dx.doi.org/10.3390/s19204419 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nguyen, Tran Thien Dat
Kim, Du Yong
GLMB Tracker with Partial Smoothing
title GLMB Tracker with Partial Smoothing
title_full GLMB Tracker with Partial Smoothing
title_fullStr GLMB Tracker with Partial Smoothing
title_full_unstemmed GLMB Tracker with Partial Smoothing
title_short GLMB Tracker with Partial Smoothing
title_sort glmb tracker with partial smoothing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832553/
https://www.ncbi.nlm.nih.gov/pubmed/31614812
http://dx.doi.org/10.3390/s19204419
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