Abstract
Laplacian eigenmap is used for motion dimension reduction. Eight Kinds of artificially typical 2D motion data are made as input of Laplacian eigenmap, and meaningful conclusions are obtained, which can be easily extended to complex data analysis in real applications. Advanced motion analysis methods have also been discussed briefly, such as 3D motion analysis, view independent motion analysis, pose representation and estimation as well as structural video analysis.
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