In daily life, human beings rely on hands and body parts to complete particular actions cooperatively. These selected body parts and their cooperative relationships are essential cues to distinguish these actions. However, most existing action recognition methods, which try to model the body appearance or spatial relations in skeleton sequences, often ignore the essential cooperation relationship among joints. Differently, in this paper, we propose a spatio-temporal hierarchical soft quantization method to extract the congenerous motion features, which reflect the cooperation relations among joints and body parts. Specifically, we design a hierarchical network with multiple soft quantization layers to extract congenerous features. The hierarchical network not only models the spatial hierarchy of skeleton structure for joint, part, and body, but also extracts the temporal hierarchy with sliding windows for frame, fragment, and sequence. Moreover, the features in each layer are visually explainable, which reflect the cooperation among body parts. The trainable parameters in the network are also significantly reduced, which reduces computational cost. Extensive experiments conducted on four benchmarks demonstrate that our method can provide competitive results compared with state-of-the-arts. The visualized congenerous features also validate that our approach can effectively perceive the essential cooperation relations.

中文翻译：

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基于骨架的人类动作识别的层次软量化

在日常生活中，人类依靠手和身体部位来协同完成特定的动作。这些选定的身体部位及其合作关系是区分这些动作的必要线索。但是，大多数现有的动作识别方法试图模拟骨架序列中的身体外观或空间关系，但往往忽略了关节之间的基本协作关系。与此不同，本文提出了一种时空分层软量化方法来提取同类运动特征，该特征反映了关节与身体部位之间的协作关系。具体来说，我们设计了一个具有多个软量化层的分层网络，以提取同类特征。层次网络不仅为关节，零件和身体建模骨架结构的空间层次，而且还会提取带有滑动窗口的时间层次结构，以用于帧，片段和序列。而且，每一层的特征在视觉上都是可以解释的，反映了身体各部位之间的协作。网络中的可训练参数也显着减少，从而降低了计算成本。在四个基准上进行的广泛实验表明，与最新技术相比，我们的方法可以提供有竞争力的结果。可视化的同类功能还验证了我们的方法可以有效地感知基本的合作关系。从而降低了计算成本。在四个基准上进行的广泛实验表明，与最新技术相比，我们的方法可以提供有竞争力的结果。可视化的同类功能还验证了我们的方法可以有效地感知基本的合作关系。从而降低了计算成本。在四个基准上进行的广泛实验表明，与最新技术相比，我们的方法可以提供有竞争力的结果。可视化的同类功能还验证了我们的方法可以有效地感知基本的合作关系。