基于3D骨架相似性的自适应移位图卷积神经网络人体行为识别算法

Human Action Recognition Algorithm Based on Adaptive Shifted Graph Convolutional Neural Network with 3D Skeleton Similarity

图卷积神经网络(Graph Convolutional Neural network,GCN)在基于3D骨架的人体行为识别领域取得了良好效果。然而,现有的大多数GCN方法对行为动作图的构建都是基于人体物理结构的手动设置,训练阶段各个图节点只能根据手动设置建立联系,无法感知动作行为过程中骨骼节点之间产生的新联系,导致图拓扑结构不合理和不灵活。移位图卷积网络通过改变图网络结构使得感受野更加灵活,并且在全局移位角度取得了良好效果。因此,提出了一种基于自适应移位图卷积神经网络(Adaptive Shift Graph Convolutional Neural network,AS-GCN)的人体行为识别算法来弥补前述GCN方法的不足。AS-GCN借鉴了移位图卷积网络的思想,提出用每个人体动作的本身特点来指导图神经网络进行移位操作,以尽可能准确地选定需要扩大感受野的节点。在基于骨架的通用动作识别数据集NTU-RGBD上,所提算法在骨骼有无物理关系约束的前提条件下均进行了实验验证。与现有的先进算法相比,AS-GCN算法的动作识别准确率在有骨骼物理约束的条件下的CV和CS角度上平均提高了12%和4.84%;在无骨骼物理约束的条件下的CV和CS角度上平均提高了20%和14.49%。

Graph convolutional neural network(GCN)has achieved good results in the field of human action recognition based on 3D skeleton.However,in most of the existing GCN methods,the construction of the behavior diagram is based on the manual setting of the physical structure of the human body.In the training stage,each graph node can only establish the connection accor-ding to the manual setting,which cannot perceive new connections between bone nodes during action,resulting in the unreasonable and inflexible topology of the graph.The shifted graph convolutional neural network(Shift-GCN)makes the receptive field more flexible by changing its structure,and achieves satisfied results in the global shift angle.In order to tackle the above pro-blems of graph structure,an adaptive shift graph convolutional neural network(AS-GCN)is proposed to make up for the above shortcomings.AS-GCN draws on the idea of shifted graph convolutional neural network,and proposes to use the characteristics of each human action to guide the graph network to perform shift operation,so as to select the nodes that need to expand the receptive field as accurately as possible.On the general skeleton-based action recognition dataset NTU-RGBD,the AS-GCN is verified by extensive experiments under the premise of whether the skeleton has physical relationship constraints or not.Compared with the existing advanced algorithms,the accuracy of action recognition of AS-GCN is improved by 12%and 4.84%respectively in CV and CS angles on average with skeleton physical constraints.While under the condition of no skeleton physical constraint,the average improvement is 20%and 14.49%in CV and CS angles,respectively.