Action Classification in Soccer Videos with Long Short-Term Memory Recurrent Neural Networks

Abstract. In this paper, we propose a novel approach for action classification in soccer videos using a recurrent neural network scheme. Thereby, we extract from each video action at each timestep a set of features which describe both the visual content (by the mean of a BoW approach) and the dominant motion (with a key point based approach). A Long Short-Term Memory-based Recurrent Neural Network is then trained to classify each video sequence considering the temporal evolution of the features for each timestep. Experimental results on the MICC-Soccer-Actions-4 database show that the proposed approach outperforms classification methods of related works (with a classification rate of 77 %), and that the combination of the two features (BoW and dominant motion) leads to a classification rate of 92 %.

• Type: Conference
• Year: 2010
• Where: ICANN
• Authors: Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, Atilla Baskurt
• Keywords: Approach Outperforms Classification, Dominant Motion, ICANN 2010, Neural Networks, Recurrent Neural Network