Generalized Laplacian Eigenmaps for Modeling and Tracking Human Motions
Jesús Martínez del Rincón, Michał Lewandowski, Jean‐Christophe Nebel, Dimitrios Makris · IEEE Transactions on Cybernetics · 2014
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.