Master's Thesis : Deep learning for robust soccer pitch localization
Bolle, Chloé
Promoteur(s) : Van Droogenbroeck, Marc
Date de soutenance : 25-jui-2020/26-jui-2020 • URL permanente : http://hdl.handle.net/2268.2/9044
Détails
Titre : | Master's Thesis : Deep learning for robust soccer pitch localization |
Auteur : | Bolle, Chloé |
Date de soutenance : | 25-jui-2020/26-jui-2020 |
Promoteur(s) : | Van Droogenbroeck, Marc |
Membre(s) du jury : | Deliège, Adrien
Barnich, Olivier |
Langue : | Anglais |
Nombre de pages : | 88 |
Discipline(s) : | Ingénierie, informatique & technologie > Ingénierie électrique & électronique |
Institution(s) : | Université de Liège, Liège, Belgique |
Diplôme : | Master : ingénieur civil électricien, à finalité spécialisée en "signal processing and intelligent robotics" |
Faculté : | Mémoires de la Faculté des Sciences appliquées |
Résumé
[en] Sport field localization has many applications such as generation of statistics, autonomous camera systems or automated generation of highlights. Nowadays, not only large federations like FIFA are interested in the calibration of autonomous and manually steered cameras, but also smaller clubs want to have such a system for analyzing matches and making strategies. For all these applications, it is essential to have a robust pitch localization even in poor conditions, for example low lighting and inconspicuous lines.
EVS has developed an autonomous calibration system. This system is based on a neural network technique. However, during a Standard-Anderlecht soccer game filmed in poor conditions, the system repeatedly failed to localize the soccer pitch. The objective of this master thesis is to solve this problem in order to localize the soccer pitch even in such bad conditions.
Fichier(s)
Document(s)
Description:
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Annexe(s)
Description: Initial prediction of the neural network trained with images of the soccer pitch
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Description: Prediction of the neural network trained with images of the soccer pitch after the modifications proposed in the master thesis
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Description: Final calibration obtained using the inferred mask of the modified neural network
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