Sequential deep learning for human action recognition
Human Behavior Understanding: Second International Workshop, HBU 2011 …, 2011•Springer
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 …
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 …
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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