Sparse Shift-Invariant Representation of Local 2D Patterns and Sequence Learning for Human Action Recognition

Moez Baccouche 1 Franck Mamalet Christian Wolf 1 Christophe Garcia 1 Atilla Baskurt 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Most existing methods for action recognition mainly rely on manually engineered features which, despite their good performances, are highly problem dependent. We propose in this paper a fully automated model, which learns to classify human actions without using any prior knowledge. A convolutional sparse auto- encoder learns to extract sparse shift-invariant representations of the 2D local patterns present in each video frame. The evolution of these mid-level features is learned by a Recurrent Neural Network trained to classify each sequence. Experimental results on the KTH dataset show that the proposed approach outperforms existing models which rely on learned-features, and gives comparable results with the best related works.
Type de document :
Communication dans un congrès
IEEE. 21st International Conference on Pattern Recognition (ICPR), Nov 2012, Tsukuba Science City, Japan. IEEE, pp.3823-3826, 2012
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https://hal.archives-ouvertes.fr/hal-01353014
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : mercredi 10 août 2016 - 16:19:04
Dernière modification le : lundi 10 décembre 2018 - 17:47:39

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  • HAL Id : hal-01353014, version 1

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Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, Atilla Baskurt. Sparse Shift-Invariant Representation of Local 2D Patterns and Sequence Learning for Human Action Recognition. IEEE. 21st International Conference on Pattern Recognition (ICPR), Nov 2012, Tsukuba Science City, Japan. IEEE, pp.3823-3826, 2012. 〈hal-01353014〉

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