  Deep Learning architectures, albeit successful in most computer vision tasks,
were designed for data with an underlying Euclidean structure, which is not
usually fulfilled since pre-processed data may lie on a non-linear space. In
this paper, we propose a geometry aware deep learning approach for
skeleton-based action recognition. Skeleton sequences are first modeled as
trajectories on Kendall's shape space and then mapped to the linear tangent
space. The resulting structured data are then fed to a deep learning
architecture, which includes a layer that optimizes over rigid and non rigid
transformations of the 3D skeletons, followed by a CNN-LSTM network. The
assessment on two large scale skeleton datasets, namely NTU-RGB+D and NTU-RGB+D
120, has proven that proposed approach outperforms existing geometric deep
learning methods and is competitive with respect to recently published
approaches.
