论文标题
使用体内和体内图的有效，准确的基于骨架的两人互动识别
Efficient and Accurate Skeleton-Based Two-Person Interaction Recognition Using Inter- and Intra-body Graphs
论文作者
论文摘要
随着姿势估计和图形卷积网络的进步，基于骨架的两人互动识别已引起人们的关注。尽管准确性逐渐提高，但日益增长的计算复杂性使其在现实环境中更不切实际。由于常规方法不能完全代表体间关节之间的关系，因此仍然存在准确性改善的空间。在本文中，我们提出了一个轻巧的模型，用于准确识别两人的相互作用。除了结合了中间融合的体系结构外，我们还引入了一种分解卷积技术，以减少模型的重量参数。我们还引入了一个网络流，该网络是体内关节之间相对距离变化以提高准确性的。使用两个大尺度数据集NTU RGB+D 60和120的实验表明，与常规方法相比，我们的方法同时达到了最高的精度和相对较低的计算复杂性。
Skeleton-based two-person interaction recognition has been gaining increasing attention as advancements are made in pose estimation and graph convolutional networks. Although the accuracy has been gradually improving, the increasing computational complexity makes it more impractical for a real-world environment. There is still room for accuracy improvement as the conventional methods do not fully represent the relationship between inter-body joints. In this paper, we propose a lightweight model for accurately recognizing two-person interactions. In addition to the architecture, which incorporates middle fusion, we introduce a factorized convolution technique to reduce the weight parameters of the model. We also introduce a network stream that accounts for relative distance changes between inter-body joints to improve accuracy. Experiments using two large-scale datasets, NTU RGB+D 60 and 120, show that our method simultaneously achieved the highest accuracy and relatively low computational complexity compared with the conventional methods.
