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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Sequential Deep Learning for Human Action Recognition
verfasst von : Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, Atilla Baskurt
Erschienen in: Human Behavior Understanding
Verlag: Springer Berlin Heidelberg
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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.