Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter

Detalhes bibliográficos
Autor(a) principal: R. R. Pinho
Data de Publicação: 2007
Outros Autores: J. M. R. S Tavares, M. F. V. Correia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://repositorio-aberto.up.pt/handle/10216/348
Resumo: In this paper, we address the problem of tracking feature points along image sequences efficiently. Thus, to estimate the undergoing movement we use an approach based on Kalman filtering, which performs the prediction and correction of the features movement in every image frame. Measured data is incorporated by optimizing the global association set built on efficient approximations of the Mahalanobis distance (MD). We analyze the difference between the usage in the tracking results of the original MD formulation and its more efficient approximation, as well as the related computational costs. Experimental results which validate our approach are presented.
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spelling Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman FilterEngenhariaEngineeringIn this paper, we address the problem of tracking feature points along image sequences efficiently. Thus, to estimate the undergoing movement we use an approach based on Kalman filtering, which performs the prediction and correction of the features movement in every image frame. Measured data is incorporated by optimizing the global association set built on efficient approximations of the Mahalanobis distance (MD). We analyze the difference between the usage in the tracking results of the original MD formulation and its more efficient approximation, as well as the related computational costs. Experimental results which validate our approach are presented.20072007-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/348eng1726452910.2507/IJSIMM06(2)S.03R. R. PinhoJ. M. R. S TavaresM. F. V. Correiainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T13:40:38Zoai:repositorio-aberto.up.pt:10216/348Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:45:26.877536Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter
title Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter
spellingShingle Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter
R. R. Pinho
Engenharia
Engineering
title_short Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter
title_full Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter
title_fullStr Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter
title_full_unstemmed Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter
title_sort Efficient Approximation of the Mahalanobis Distance for Tracking with the Kalman Filter
author R. R. Pinho
author_facet R. R. Pinho
J. M. R. S Tavares
M. F. V. Correia
author_role author
author2 J. M. R. S Tavares
M. F. V. Correia
author2_role author
author
dc.contributor.author.fl_str_mv R. R. Pinho
J. M. R. S Tavares
M. F. V. Correia
dc.subject.por.fl_str_mv Engenharia
Engineering
topic Engenharia
Engineering
description In this paper, we address the problem of tracking feature points along image sequences efficiently. Thus, to estimate the undergoing movement we use an approach based on Kalman filtering, which performs the prediction and correction of the features movement in every image frame. Measured data is incorporated by optimizing the global association set built on efficient approximations of the Mahalanobis distance (MD). We analyze the difference between the usage in the tracking results of the original MD formulation and its more efficient approximation, as well as the related computational costs. Experimental results which validate our approach are presented.
publishDate 2007
dc.date.none.fl_str_mv 2007
2007-01-01T00:00:00Z
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dc.identifier.uri.fl_str_mv https://repositorio-aberto.up.pt/handle/10216/348
url https://repositorio-aberto.up.pt/handle/10216/348
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 17264529
10.2507/IJSIMM06(2)S.03
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