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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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099.1/21364 |
| Acceso en línea: | https://hdl.handle.net/2099.1/21364 |
| Access Level: | acceso abierto |
| Palabra clave: | 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 |
| Sumario: | 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. |
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