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.
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Zhao, F., Ma, S., Hao, Z., Wen, J. (2014). Gait Pose Estimation Based on Manifold Learning. In: Ma, S., Jia, L., Li, X., Wang, L., Zhou, H., Sun, X. (eds) Life System Modeling and Simulation. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45283-7_9
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DOI: https://doi.org/10.1007/978-3-662-45283-7_9
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