In recent years, the topic of skeleton‐based human action recognition has attracted significant attention from researchers and practitioners in graphics, vision, animation, and virtual environments. The most fundamental issue is how to learn an effective and accurate representation from spatiotemporal action sequences towards improved performance, and this article aims to address the aforementioned challenge. In particular, we design a novel method of hybrid features' extraction based on the construction of multistream networks and their organic fusion. First, we train a convolution neural networks (CNN) model to learn CNN‐based features with the raw skeleton coordinates and their temporal differences serving as input signals. The attention mechanism is injected into the CNN model to weigh more effective and important information. Then, we employ long short‐term memory (LSTM) to obtain long‐term temporal features from action sequences. Finally, we generate the hybrid features by fusing the CNN and LSTM networks, and we classify action types with the hybrid features. The extensive experiments are performed on several large‐scale publically available databases, and promising results demonstrate the efficacy and effectiveness of our proposed framework.

中文翻译：

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基于网络融合的基于骨架的动作识别的混合特征

近年来，基于骨架的人体动作识别主题引起了图形、视觉、动画和虚拟环境领域的研究人员和从业人员的极大关注。最基本的问题是如何从时空动作序列中学习有效且准确的表示以提高性能，本文旨在解决上述挑战。特别是，我们设计了一种基于多流网络的构建及其有机融合的混合特征提取的新方法。首先，我们训练一个卷积神经网络 (CNN) 模型，以原始骨架坐标及其时间差异作为输入信号来学习基于 CNN 的特征。将注意力机制注入到 CNN 模型中，以权衡更有效和重要的信息。然后，我们使用长短期记忆（LSTM）从动作序列中获取长期时间特征。最后，我们通过融合 CNN 和 LSTM 网络生成混合特征，并使用混合特征对动作类型进行分类。在几个大型公开数据库上进行了广泛的实验，有希望的结果证明了我们提出的框架的有效性和有效性。