论文标题
基于骨架的动作识别的多尺度时间图网络
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition
论文作者
论文摘要
图形卷积网络（GCN）可以有效地捕获相关节点的特征并改善模型的性能。在基于骨架的动作识别中使用GCN更加关注。但是基于GCN的现有方法有两个问题。首先，对于按节点和帧提取特征节点，忽略了时间和空间特征的一致性。为了同时获得时空特征，我们设计了用于动作识别的骨架序列的一般表示，并提出了一种称为暂时图网络（TGN）的新型模型。其次，描述关节关系的图表的邻接矩阵主要取决于关节之间的物理连接。为了适当描述骨骼图中的关节之间的关系，我们提出了一个多规模的图形策略，采用了全尺度图，零件尺度图和核心尺度图，以捕获每个关节的局部特征以及重要关节的轮廓特征。在两个大数据集上进行了实验，结果表明，使用图形策略的TGN优于最先进的方法。
Graph convolutional networks (GCNs) can effectively capture the features of related nodes and improve the performance of the model. More attention is paid to employing GCN in Skeleton-Based action recognition. But existing methods based on GCNs have two problems. First, the consistency of temporal and spatial features is ignored for extracting features node by node and frame by frame. To obtain spatiotemporal features simultaneously, we design a generic representation of skeleton sequences for action recognition and propose a novel model called Temporal Graph Networks (TGN). Secondly, the adjacency matrix of the graph describing the relation of joints is mostly dependent on the physical connection between joints. To appropriately describe the relations between joints in the skeleton graph, we propose a multi-scale graph strategy, adopting a full-scale graph, part-scale graph, and core-scale graph to capture the local features of each joint and the contour features of important joints. Experiments were carried out on two large datasets and results show that TGN with our graph strategy outperforms state-of-the-art methods.
