KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition

• URL: http://arxiv.org/abs/2011.12004v1
• Date: Tue, 24 Nov 2020 10:14:07 GMT
• Title: KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition
• Authors: Racha Friji, Hassen Drira, Faten Chaieb, Sebastian Kurtek, Hamza Kchok
• Abstract summary: We propose a geometry aware deep learning approach for skeleton-based action recognition. Skeletons 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 optimize over rigid and non rigid transformations.
• Score: 7.183483982542308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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