Generalized Laplacian Eigenmaps for Modeling and Tracking Human Motions

Jesus Martinez-del-Rincon, Michal Lewandowski, Jean-Christophe Nebel, Dimitrios Makris

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1646-1660
Number of pages15
JournalIEEE Transactions on Cybernetics
Issue number9
Early online date11 Dec 2013
Publication statusPublished - Sep 2014

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