title: Generalized Laplacian eigenmaps for modeling and tracking human motions.
abstract: This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.
authors.forename: Jesus
authors.initials: J
authors.lastname: Martinez-del-Rincon
authors.forename: Michal
authors.initials: M
authors.lastname: Lewandowski
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Nebel
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Makris
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D005684:Gait / Q000502:physiology; D006801:Humans; D008297:Male; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated; D011187:Posture / Q000502:physiology; D015203:Reproducibility of Results; D055815:Young Adult