Locally time-invariant models of human activities using trajectories on the grassmannian

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Deep Learning on Lie Groups for Skeleton-Based Action Recognition (2016, methodology): “Typical examples include shape silhouettes in the Kendall’s shape space [40, 3], linear dynamical systems on the Grassmann manifold [39], histograms of oriented optical flow on a hyper-sphere [11], and pairwise transformations of skeletal joints on a Lie group [41, 3, 42].”
Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (2016, background): “These approaches can be mainly divided into two perspectives, namely (i) generative models that assume the skeleton points are produced by a latent dynamic model [2] corrupted by noise and (ii) discriminative approaches that generate compact representations of sequences on which classifiers are trai”
Grassmannian Representation of Motion Depth for 3D Human Gesture and Action Recognition (2014, methodology): “Such state-of-the-art manifold techniques is presented by Turaga et al. [14], [15].”
Human gesture recognition on product manifolds (2012, background): “Turaga and Chellappa (2009) investigated statistical modeling with AR models for human activity analysis.”