ICSEE 2014, LSMS 2014: Life System Modeling and Simulation pp 82-90 | Cite as
Gait Pose Estimation Based on Manifold Learning
Abstract
A manifold learning based approach for gait pose estimation is proposed in this paper. It consists of two manifold learning based dimension reductions and three mapping functions based on General Regression Neural Network (GRNN). A model of various people walking gait is built so as to find the correspondence between a new gait pose image and the model. The reduced low-dimensional data can be used to realize the mapping between 2D gait pose model and 3D body configuration. When inputting a 2D gait pose image, it can provide the corresponding pose image in the model which can be used to carry out the mapping by the trained GRNN. Simulated experiments manifested the effectiveness of the approach.
Keywords
Pose estimation manifold learning dimension reduction GRNNPreview
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