An adaptive stacked denoising auto-encoder architecture for human action recognition
Wu, Dao Xi
Xie, Li Dong
Huang, Chao Xi
- 信息技术－会议论文 
In this paper, a stacked denoising auto-encoder architecture method with adaptive learning rate for action recognition based on skeleton features is presented. Firstly a Kinect is used for capturing the skeleton images and extracting skeleton features. Then an adaptive stacked denoising auto-encoder with three hidden layers is constructed for unsupervised pre-training. So the trained weights are achieved. Finally, a neural network is constructed for action recognition, in which the trained weights are used as the initial value, covering the random value. Based on the experimental results from the Kinect dataset of human actions sampled in experiments, it is clear to see that our method possesses better robustness and accuracy, compared with the classic classification methods. ? 2014 Trans Tech Publications, Switzerland.