基于图卷积网络改进的人体动作识别模型

针对双流自适应图卷积动作识别网络2S-AGCN模型忽略了人体动作识别中特征的长距离信息以及通道之间的依赖的缺点,设计了一种双重注意力机制对2S-AGCN模型的图卷积模块进行改进,实现精度的提升。双重注意力机制包含了空间注意力机制以及通道注意力机制,其中空间注意力机制有选择性地聚集上下文,通道注意力机制分为两个并行的模块,第1部分提高了特征的可辨性,第2部分在捕获特征远程依赖的同时,保留了精准的位置信息。结果表明,以双流自适应图卷积动作识别网络2S-AGCN模型为基础网络,融入了双重注意力机制模块的模型在Kinetics数据集上的Top-1和Top-5分别提升了0.6%和1.3%,在NTURGB+D120数据集的CS和CV上的Top-1分别提升了1.2%和0.5%,在NTURGB+D数据集的CS和CV上的Top-1分别提了0.2%和0.1%。

Improved human action recognition model based on graph convolutional networks

In view of the shortcomings that the 2S-AGCN model of the two-stream adaptive graph convolutional network ignores the long-distance information of features and channel dependence in human motion recognition, a dual attention mechanism is designed to improve the graph convolution module of the 2S-AGCN model to improve the accuracy. The dual attention mechanism includes the spatial attention mechanism and the channel attention mechanism. The spatial attention mechanism selectively focuses on the context. The channel attention mechanism is divided into two parallel modules. The first part improves the distinguishability of features. The second part preserves accurate location information while capturing the remote dependency of features. The results show that the model based on the two-stream adaptive graph convolutional networks 2S-AGCN, which incorporates the dual attention mechanism module, has improved Top-1 and Top-5 on the Kinetics dataset by 0.6 and 1.3 percentage points respectively, Top-1 on the CS and CV of NTURGB+D120 dataset by 1.2 and 0.5 percentage points respectively, and Top-1 on the CS and CV of NTURGB+D dataset by 0.2 and 0.1 percentage points respectively.