Geometric Deep Neural Network using Rigid and Non-Rigid Transformations for Human Action Recognition

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 using rigid and non rigid transformation optimization 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 the proposed approach outperforms existing geometric deep learning methods and exceeds recently published approaches with respect to the majority of configurations.

Cross-view action recognition using transformers

“…In [6], Chen et al proposed a channel-wise topology refinement graph convolution. Friji et al [11] used Kendall's shape analysis while Li et al [22] used elastic semantic nets. Nguyen [29] proposed to represent skeleton sequences using sets of SPD matrices.…”