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Deep Learning on Lie Groups for Skeleton-based Action Recognition

Zhiwu Huang, Chengde Wan, Thomas Probst, Luc Van Gool
Computer Vision and Pattern Recognition (CVPR)
July 2017

Abstract

In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the- art methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features. In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition. Within the network structure, we design rota- tion mapping layers to transform the input Lie group fea- tures into desirable ones, which are aligned better in the temporal domain. To reduce the high feature dimensional- ity, the architecture is equipped with rotation pooling layers for the elements on the Lie group. Furthermore, we propose a logarithm mapping layer to map the resulting manifold data into a tangent space that facilitates the application of regular output layers for the final classification. Evalua- tions of the proposed network for standard 3D human ac- tion recognition datasets clearly demonstrate its superiority over existing shallow Lie group feature learning methods as well as most conventional deep learning methods.


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@InProceedings{eth_biwi_01382,
  author = {Zhiwu Huang and Chengde Wan and Thomas Probst and Luc Van Gool},
  title = {Deep Learning on Lie Groups for Skeleton-based Action Recognition},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
  month = {July},
  keywords = {}
}