基于样本的等距特征映射的行为分析方法

Motion Capture Data Analysis Based on Sampling-Isomap

  • 摘要: 针对运动捕捉数据分析和应用的效率要求,提出了一种基于样本-Isomap的行为分析方法. 通过计算运动数据中样本帧的距离矩阵得到样本嵌入空间的特征向量,用其近似表示嵌入空间的特征向量,然后在该空间上计算非样本帧的投影,得到非样本帧的近似流形嵌入. 结果表明当样本帧的选取比例在10%时可以近似得到整个运动数据的低维流形嵌入,且处理效率比原方法提高10倍以上. 应用该算法对高维运动捕捉数据进行降维,能够提高运动捕捉数据分析和应用的效率.

     

    Abstract: A method of motion data processing based on sampling-Isomap is proposed. By computing the distance matrix of sample frames, the sample embeddings eigenvectors can be obtained to approximate the eigenvectors of the original embeddings, and then the non-sample frames can be projected in this embeddings to approach the manifold embeddings of non-sample frames. Experiments proved that approximate embeddings of motion data computed by sampling-Isomap were average 10 times faster than by Isomap, while 10% frame samples were selected. Motion analysis can be performed efficiently even with high-dimensionality of motion capture data.

     

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