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Kernel analysis on Grassmann manifolds for action recognition
2013
Pattern Recognition Letters
Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds
doi:10.1016/j.patrec.2013.01.008
fatcat:n42extttsngaflqdmqvzxuozte