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ICANN
2010
Springer

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

13 years 4 months ago
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 %.
Moez Baccouche, Franck Mamalet, Christian Wolf, Ch
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICANN
Authors Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, Atilla Baskurt
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