Team activity recognition in Association Football using a Bag-of-Words-based method
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Scholar |
Otros documentos de la autoría: Montoliu Colás, Raul; Martín Félez, Raúl; Torres-Sospedra, Joaquín; Martínez Usó, Adolfo
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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http://dx.doi.org/10.1016/j.humov.2015.03.007 |
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Título
Team activity recognition in Association Football using a Bag-of-Words-based methodFecha de publicación
2015-06Editor
ElsevierCita bibliográfica
MONTOLIU, Raúl, et al. Team activity recognition in Association Football using a Bag-of-Words-based method. Human movement science, 2015, vol. 41, p. 165-178.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0167945715000494Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
In this paper, a new methodology is used to perform team activity recognition and analysis in Association Football. It is based on pattern recognition and machine learning techniques. In particular, a strategy based ... [+]
In this paper, a new methodology is used to perform team activity recognition and analysis in Association Football. It is based on pattern recognition and machine learning techniques. In particular, a strategy based on the Bag-of-Words (BoW) technique is used to characterize short Football video clips that are used to explain the team’s performance and to train advanced classifiers in automatic recognition of team activities. In addition to the neural network-based classifier, three more classifier families are tested: the k-Nearest Neighbor, the Support Vector Machine and the Random Forest. The results obtained show that the proposed methodology is able to explain the most common movements of a team and to perform the team activity recognition task with high accuracy when classifying three Football actions: Ball Possession, Quick Attack and Set Piece. Random Forest is the classifier obtaining the best classification results. [-]
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Human movement science, 2015, vol. 41Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/restrictedAccess
info:eu-repo/semantics/restrictedAccess
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