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              <p>Dans cet article, nous proposons une approche de classification automatique de séquences vidéo d’actions de sport. Pour cela, nous extrayons de chaque action des caractéristiques du contenu visuel, en utilisant deux approches, l’une par sac de mots, et l’autre par le mouvement dominant de la scène à chaque instant. La classification de l’évolution temporelle de ces caractéristiques extraites est gérée dynamiquement par un modèle neuronal, basé sur les réseaux de neurones récurrents à large « mémoire court-terme» (LSTM). Les expérimentations faites sur la base « MICCSoccer-Actions-4 » montrent que l’approche neuronale de classification permet d’obtenir des résultats supérieurs à l’état de l’art (76 % de bonne classification), et que la combinaison des caractéristiques (information visuelle et mouvement dominant) permet un taux de bonne classification de 92 %.</p>
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