目的 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。方法 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。结果 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%。结论 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。

Objective 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.Method 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.Result 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%. Conclusion The analysis of experimental Results and the comparison with baseline Methods demonstrate the generality and efficiency of our Method.