Human action recognition is the basis technology of human behavior understanding, and it is a research hotspot in the field of computer vision. Recently, some studies show skeleton data (i.e., joint points and edges) is naturally more conducive to mining the connotation of human action, so exploring the relationship between joints and bones is helpful to improve action recognition. In this article, regarding skeleton data as a directed graph, we design directed graph convolutional neural networks with a novel residual split structure. First, we construct a directed graph represent model to extract human behavior by two kinds of graph models. Second, we use a novel residual split block to construct graph convolution neural network. Different from the traditional residual networks, we split high-dimensional features into several shallow features with the same dimension. It can not only ensure the diversity of mining features, but also avoid the gradient disappearing. Finally, during training, we use random sampling of data to reduce the burden of network training. Experiment results show that the proposed method achieves higher recognition rate than the comparative methods on the NTU-RGBD dataset.

中文翻译：

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用于动作识别的深度残差拆分有向图卷积神经网络

人体动作识别是人类行为理解的基础技术，是计算机视觉领域的研究热点。最近，一些研究表明骨骼数据（即关节点和边缘）自然更有利于挖掘人类动作的内涵，因此探索关节和骨骼之间的关系有助于改善动作识别。在本文中，将骨架数据作为有向图，我们设计了具有新颖残差拆分结构的有向图卷积神经网络。首先，我们构建了一种有向图表示模型，通过两种图模型来提取人类行为。其次，我们使用一种新颖的残差拆分块来构造图卷积神经网络。与传统的残差网络不同，我们将高维特征分为具有相同维的几个浅层特征。它不仅可以保证挖掘特征的多样性，而且可以避免梯度的消失。最后，在训练过程中，我们使用数据的随机抽样来减轻网络训练的负担。实验结果表明，与NTU-RGBD数据集上的比较方法相比，该方法具有更高的识别率。