Graph regularized implicit pose for 3D human action recognition

We present a novel feature descriptor for 3D human action recognition using graph signal processing techniques. A linear subspace is learned using graph total variation and graph Tikhonov regularizers, transforming 3D time derivative information into a representation that is robust against noisy skeleton measurements. The graph total variation regularizer learns an action representation that encourages piece-wise constantness, which helps discriminating between different action classes. Graph Tikhonov regularization ensures the searched low-rank subspace is similar to the original feature. Experiments show that our approach learns a good representation of an action due to the explicit graph structure, and achieves a statistically significant improvement over the baseline moving pose method, resulting in a 93.5% accuracy on the challenging MSRAction3D dataset.