Unsupervised Dance Motion Patterns Classification from Fused Skeletal Data using Exemplar-based HMMs
Unsupervised Dance Motion Patterns Classification from Fused Skeletal Data using Exemplar-based HMMs
复制标题
使用基于 Exemplar 的 HMM 对融合骨骼数据进行无监督舞蹈运动模式分类
DOI：
10.1260/2047-4970.4.2.209
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
N. Grammalidis
中科院分区：
文献类型：
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作者：
A. Kitsikidis;N. Boulgouris;K. Dimitropoulos;N. Grammalidis
在本文中，我们提出了一种将舞蹈序列分割成多个周期和运动模式的方法。所提出的方法使用多个深度传感器以随时间观察的舞者的骨架表示的形式部署特征。这种表示是使用多个传感器捕获的骨骼特征的融合，并组合成单一的、更健壮的骨骼表示。使用该信息，我们首先将舞蹈序列划分为周期，然后将其划分为运动模式。分割成周期是基于观察舞者的水平位移，而每个周期随后通过使用基于样本的隐马尔可夫模型被分割成运动模式，该模型将每一帧分类成表示HMM的隐藏状态的样本。提出的方法在包含多个周期和动作模式的舞蹈序列上进行了测试，得到了令人满意的结果。
In this paper, we propose a method for the partitioning of dance sequences into multiple periods and motion patterns. The proposed method deploys features in the form of a skeletal representation of the dancer observed through time using multiple depth sensors. This representation is the fusion of skeletal features captured using multiple sensors and combined into a single, more robust, skeletal representation. Using this information, initially we partition the dance sequence into periods and subsequently into motion patterns. Partitioning into periods is based on observing the horizontal displacement of the dancer while each period is subsequently partitioned into motion patterns by using an exemplar-based Hidden Markov Model that classifies each frame into an exemplar representing a hidden state of the HMM. The proposed method was tested on dance sequences comprising multiple periods and motion patterns providing promising results.