Abstract
The segmentation of motion capture data is essential for the synthesis of motion data, its purpose is to split long movement sequence data into many different independent semantic motion clips, and it requires that the segmentation of motion capture data is effective and accurate. This paper proposed a segmentation algorithm of motion capture data based on measured MDS and improved oblique space distance. The proposed approach used the multidimensional scaling (MDS) to achieve the space mapping from original high-dimensional data to low-dimensional, and then calculated the improved oblique space distance between frames in the specified windows and the preceding section in the low-dimensional space, and obtained the final segmentation points by similarity detection. Finally we obtained the independent semantic motion clips, and we verified the feasibility of the algorithm through experiments, and the accuracy rate of our method is improved compared with the traditional algorithm.
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Acknowledge
This work is supported by the National Natural Science Foundation of China (No.61370141, 61300015), Natural Science Foundation of Liaoning Province (No. 2013020007), the Scientific Research Fund of Liaoning Provincial Education Department (No. L2013459, L2015015), the Program for Science and Technology Research in New Jinzhou District (No. KJCX-ZTPY-2014-0012).
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Song, D., Dong, J., Zhang, Q. (2015). Segmentation of Motion Capture Data Based on Measured MDS and Improved Oblique Space Distance. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_17
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DOI: https://doi.org/10.1007/978-3-319-26181-2_17
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