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Sequential Deep 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 : We propose in this paper a fully automated deep model, which learns to classify human actions without using any prior knowledge. The first step of our scheme, based on the extension of Convolutional Neural Networks to 3D, automatically learns spatio-temporal features. A Recurrent Neural Network is then trained to classify each sequence considering the temporal evolution of the learned features for each timestep. Experimental results on the KTH dataset show that the proposed approach outperforms existing deep models, and gives comparable results with the best related works.
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https://hal.archives-ouvertes.fr/hal-01354493
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Thursday, August 18, 2016 - 7:29:56 PM
Last modification on : Wednesday, November 20, 2019 - 3:06:17 AM

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Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, Atilla Baskurt. Sequential Deep Learning for Human Action Recognition. 2nd International Workshop on Human Behavior Understanding (HBU), Nov 2011, Amsterdam, Netherlands. pp.29-39, ⟨10.1007/978-3-642-25446-8_4⟩. ⟨hal-01354493⟩

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