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Conference Papers Year : 2011

Sequential Deep Learning for Human Action Recognition


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.

Dates and versions

hal-01354493 , version 1 (18-08-2016)



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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