Abstract In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed of the learning phase. Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network. These layers are designed to solve the action recognition problem. The first-layer growing grid receives the input data of human actions and the neural map generates an action pattern vector representing each action sequence by connecting the elicited activation of the trained map. The pattern vectors are then sent to the ordered vector representation layer to build the time-invariant input vectors of key activations for the second-layer growing grid. The second-layer growing grid categorizes the input vectors to the corresponding action clusters/sub-clusters and finally the one-layer supervised neural network labels the shaped clusters with action labels. Three experiments using different datasets of actions show that the system is capable of learning to categorize the actions quickly and efficiently. The performance of the growing grid architecture is compared with the results from a system based on Self-Organizing Maps, showing that the growing grid architecture performs significantly superior on the action recognition tasks.

中文翻译：

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用于基于骨架的动作识别的分层生长网格网络

摘要在本文中，通过应用不断增长的网格神经网络层，开发了一种用于动作识别的新型认知架构。使用这些层使系统能够自动安排其表示结构。除了在成长阶段扩展神经图外，系统还提供了输入空间的先验知识，从而提高了学习阶段的处理速度。除了两层生长网格网络外，该架构还包括预处理层、有序向量表示层和一层监督神经网络。这些层旨在解决动作识别问题。第一层生长网格接收人类动作的输入数据，神经图通过连接训练图的引发激活来生成表示每个动作序列的动作模式向量。然后将模式向量发送到有序向量表示层，为第二层生长网格构建关键激活的时不变输入向量。第二层生长网格将输入向量分类到相应的动作簇/子簇，最后一层监督神经网络用动作标签标记成形的簇。使用不同动作数据集的三个实验表明，该系统能够快速有效地学习对动作进行分类。