面向智慧教育行为分析的图卷积骨架动作识别方法

Graph Convolutional Skeleton-based Action Recognition Method for Intelligent Behavior Analysis

智慧教育即教育信息化,是利用现代信息技术的新一代教育模式,智慧行为分析是智慧教育系统的核心组成。在面对复杂的教室应用场景时,针对传统的行为识别分类算法的精确性与时效性都存在严重不足的问题,提出了一种基于分离与注意力机制的图卷积(Depthwise Separable Attention Graph Convolutional Network,DSA-GCN)骨架动作识别算法。首先,为解决传统算法在通道域信息聚合天生不充分的难题,通过逐点卷积进行多维通道映射,将时空图卷积对输入骨骼序列的原始时空信息的保护能力与深度可分离卷积在空间和通道特征学习上的分离能力相结合,以增强模型特征学习与抽象表达性。其次,采用多维度融合的注意力机制,在空间卷积域利用自注意力与通道注意力机制来提升模型的动态敏感性,在时间卷积域利用时间与通道注意力融合法来增强对关键帧的判别力。实验结果表明,在NTU RGB+D和N-UCLA两个大型数据集上,DSA-GCN都获得了优异的性能和效能表现,证明了模型对通道域信息聚合能力的提升。

Smart education is a new education model using modern information technology,and smart behavior analysis is the core component.In the complex classroom scenarios,traditional action recognition algorithms are seriously deficient in accuracy and timeliness.A graph convolutional method based on separation and attention mechanism(DSA-GCN) is proposed to solve the above problems.First,in order to solve the challenge that traditional algorithms are inherently inadequate in aggregating information in the channel domain,multidimensional channel mapping is performed by point-wise convolution,combining the ability of ST-GC to preserve the original spatio-temporal information with the separation ability of depth-separable convolution in spatial and channel feature learning to enhance model feature learning and abstract expressivity.Second,a multi-dimensional fused attention mechanism is used to enhance the model dynamic sensitivity in the spatial convolution domain using self-attention and channel attention mechanisms,and to enhance the key frame discrimination in the temporal convolution domain using temporal and channel attention fusion method.Experiment results show that DSA-GCN achieves better accuracy and effectiveness performance on NTU RGB+D and N-UCLA datasets,and prove the improvement of the ability to aggregate channel information.