Master thesis : Automatic identification of football players based on game video sequences for the production of metadata
|Title :||Master thesis : Automatic identification of football players based on game video sequences for the production of metadata|
|Translated title :||[fr] Identification automatique de joueurs de football basée sur des séquences vidéo de match pour la production de métadonnées|
|Author :||Michel, Tom|
|Date of defense :||26-Jun-2017/27-Jun-2017|
|Advisor(s) :||Wehenkel, Louis
Van Droogenbroeck, Marc
|Committee's member(s) :||Geurts, Pierre
|Number of pages :||133|
|Keywords :||[en] Football|
[en] Deep learning
[en] Machine learning
[en] Computer vision
[en] Player identification
|Discipline(s) :||Engineering, computing & technology > Electrical & electronics engineering|
|Funders :||EVS Broadcast Equipment|
|Institution(s) :||Université de Liège, Liège, Belgique|
|Degree:||Master en ingénieur civil électricien, à finalité spécialisée en "electrical engineering"|
|Faculty:||Master thesis of the Faculté des Sciences appliquées|
[en] For the broadcast of live football games, a lot of people are required: the director chooses at any time the most interesting camera to show on TV while the so-called loggers encode the statistics of the match. More and more, we try to help those people by automating their tasks. To do so, artificial intelligence methods must be developed for computers to understand what happens on the pitch. In order to achieve this goal, many subproblems must be solved separately. Automatic identification of players is one of them. This master thesis tries to handle this specific problem by focusing on the players numbers. The whole work is separated into 3 main parts: player detection, digit detection and digit recognition. Those different tasks are tackled with the help of computer vision and deep learning techniques. In particular, a segmentation semantic method is implemented. This allows us to know whether a pixel in the image belongs to a player or a digit with an accuracy higher than 98%. Digit recognition also obtains very promising performances. On the whole, this master thesis offers an array of techniques for each individual task. With more training data, those techniques would give robust models that could be applied to any football game.
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Description: Folder containing the source code developed in this master thesis.
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Description: Folder containing some videos of the main results of this master thesis.
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Description: One page summary with illustrations.
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