运动捕捉数据中足迹的谱聚类检测方法

刘晓平; 陆劲挺; 谢文军

doi:10.11834/jig.20140907

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专辑

运动捕捉数据中足迹的谱聚类检测方法
Foot plant detection based on spectral clustering algorithm for motion capture data
2014年19卷第9期页码：1306-1315
网络出版：2014-09-06，

纸质出版：2014
DOI： 10.11834/jig.20140907
稿件说明：

移动端阅览

刘晓平, 陆劲挺, 谢文军. 运动捕捉数据中足迹的谱聚类检测方法[J]. 中国图象图形学报, 2014,19(9):1306-1315. DOI： 10.11834/jig.20140907.

Liu Xiaoping, Lu Jinting, Xie Wenjun. Foot plant detection based on spectral clustering algorithm for motion capture data[J]. Journal of Image and Graphics, 2014, 19(9): 1306-1315. DOI： 10.11834/jig.20140907.

摘要

从运动捕捉数据中直接检测足迹非常困难。目前，已有的方法不能在无人工交互条件下实现对任意平地运动数据的足迹自动检测，为此提出一种基于谱聚类的足迹自动检测方法，可应用于任意角色的平地运动。首先，提取角色的脚部运动特征并表示为样本。然后，分析样本模长的变化规律并自适应计算谱聚类参数。最后，使用谱聚类方法检测出足迹帧。实验应用于混合运动数据集后，足迹检测的准确性良好，检测的整体准确率可达98.72%。对实验结果的分析以及与基准线法的比较，证明了本文方法的普适性和有效性。

Abstract

It's hardly to directly detect the foot plants from motion capture data. Many previous works have already successfully found the foot plant constraints. However

none of these Methods is completely automatic without any interaction. In this paper

we present a foot plant detection Method based on spectral clustering for motion capture data. First

samples are represented by motion features of performer's feet. Second

parameters are selected by analyzing the norms of samples. Finally

foot plants are detected by spectral clustering algorithm. After applying on a blending motion data set

high accuracy rates of foot plant detection are acquired，the accuracy rates of foot plant detection can reach 98.72%. The analysis of experimental Results and the comparison with baseline Methods demonstrate the generality and efficiency of our Method.