Abstract
The segmentation of motion capture data is to separate the different types of human motion data contains long movement sequence into motion clips with independent semantics in order to facilitate the storage in the database as well as medical analysis. This paper proposed a method for human motion capture data segmentation based on Laplacian Eigenmaps (LE) algorithm. Firstly, the LE algorithm is used to reduce the dimension of original data by realizing the mapping from the high dimensional data to the low dimensional space. And then a specified window was drawn in the low dimensional space which was used to calculate the space distance from frames in the specified window to each frame in the former fragment. Finally we detected the similarity to get the final segmentation points, thus obtained motion clips with independent semantics. The validity of the segmentation method is verified by experiment.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61370141, 61300015), the Program for Dalian High-level Talent’s Innovation (2015R088).
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Xie, X., Liu, R., Zhou, D., Wei, X., Zhang, Q. (2017). Segmentation of Human Motion Capture Data Based on Laplasse Eigenmaps. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_13
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DOI: https://doi.org/10.1007/978-3-319-67964-8_13
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