Type of view estimation in Football sequences

Due to the huge repercussion of football broadcast in society, an enormous number of applications can be derived to both analyze the match and enhance the visual experience of the spectator. These applications request semantical information about the content of the images. In particular, the type of...

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Bibliographic Details
Author: Varas González, David
Format: master thesis
Publication Date:2010
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2099.1/21364
Online Access:https://hdl.handle.net/2099.1/21364
Access Level:Open access
Keyword:Images -- Classification
Image processing
classification
football
view
descriptor
decission tree
Imatges -- Classificació
Imatges -- Processament
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
id ES_f91c7df6abddc7ab7c28e075f3c4dd9a
oai_identifier_str oai:upcommons.upc.edu:2099.1/21364
network_acronym_str ES
network_name_str España
repository_id_str
spelling Type of view estimation in Football sequencesVaras González, DavidImages -- ClassificationImage processingclassificationfootballviewdescriptordecission treeImatges -- ClassificacióImatges -- ProcessamentÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeoDue to the huge repercussion of football broadcast in society, an enormous number of applications can be derived to both analyze the match and enhance the visual experience of the spectator. These applications request semantical information about the content of the images. In particular, the type of view in a football image contains valuable information about the game. Thus, the type of view must be automatically computed to be able to process the large amount of information extracted from each football match. In this work, we propose a robust classification system that estimates the type of view in football images in real time. For each frame of the sequence, a set of descriptors is extracted to characterize a specific part of the scene: the grass field. Gathering all these descriptors and a few ones related with texture, a decision tree determines the view that is shown in that frame. In order to improve the robustness of the algorithm, the redundancy of the temporal domain is exploited. The validity of the proposed algorithm has been tested on a large amount of frames from broadcasted football sequences in a wide variety of scenarios (stadiums, light conditions, ...). Promising results have been obtained with a 96% of accuracy in the classification of these images.Universitat Politècnica de CatalunyaMarqués Acosta, Fernando20102010-07-1020142014-05-12master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2099.1/21364reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099.1/213642026-05-27T15:37:01Z
dc.title.none.fl_str_mv Type of view estimation in Football sequences
title Type of view estimation in Football sequences
spellingShingle Type of view estimation in Football sequences
Varas González, David
Images -- Classification
Image processing
classification
football
view
descriptor
decission tree
Imatges -- Classificació
Imatges -- Processament
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
title_short Type of view estimation in Football sequences
title_full Type of view estimation in Football sequences
title_fullStr Type of view estimation in Football sequences
title_full_unstemmed Type of view estimation in Football sequences
title_sort Type of view estimation in Football sequences
dc.creator.none.fl_str_mv Varas González, David
author Varas González, David
author_facet Varas González, David
author_role author
dc.contributor.none.fl_str_mv Marqués Acosta, Fernando
dc.subject.none.fl_str_mv Images -- Classification
Image processing
classification
football
view
descriptor
decission tree
Imatges -- Classificació
Imatges -- Processament
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
topic Images -- Classification
Image processing
classification
football
view
descriptor
decission tree
Imatges -- Classificació
Imatges -- Processament
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
description Due to the huge repercussion of football broadcast in society, an enormous number of applications can be derived to both analyze the match and enhance the visual experience of the spectator. These applications request semantical information about the content of the images. In particular, the type of view in a football image contains valuable information about the game. Thus, the type of view must be automatically computed to be able to process the large amount of information extracted from each football match. In this work, we propose a robust classification system that estimates the type of view in football images in real time. For each frame of the sequence, a set of descriptors is extracted to characterize a specific part of the scene: the grass field. Gathering all these descriptors and a few ones related with texture, a decision tree determines the view that is shown in that frame. In order to improve the robustness of the algorithm, the redundancy of the temporal domain is exploited. The validity of the proposed algorithm has been tested on a large amount of frames from broadcasted football sequences in a wide variety of scenarios (stadiums, light conditions, ...). Promising results have been obtained with a 96% of accuracy in the classification of these images.
publishDate 2010
dc.date.none.fl_str_mv 2010
2010-07-10
2014
2014-05-12
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099.1/21364
url https://hdl.handle.net/2099.1/21364
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869425064341929984
score 15.300719

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