基于骨架特征的瓶颈层多尺度图卷积动作识别方法  

Bottleneck Multi-scale Graph Convolutional Network for Skeleton-based Action Recognition

作　　者：黄海新 王钰瑶 蔡明启 HUANG Haixin;WANG Yuyao;CAI Mingqi(School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,China)

机构地区：[1]沈阳理工大学自动化与电气工程学院,沈阳110159

出　　处：《计算机科学》2024年第S02期344-348,共5页Computer Science

基　　金：国家自然科学基金(61672359)。

摘　　要：动作识别方法在计算机视觉领域取得了显著的效果,其中图卷积网络是动作识别任务的一种重要手段,在提取图结构数据的特征中表现出了卓越优势。然而,现有的图卷积动作识别网络仍存在一些问题,如过度依赖预定义骨架拓扑图结构、大时间卷积核计算成本高且缺乏灵活性等,这些问题极大限制了模型的表达能力和鲁棒性。文中提出了一种基于骨架数据的自适应瓶颈层多尺度图卷积动作识别方法,自适应空间模块对骨架拓扑图结构和参数进行优化学习,从而增强模型灵活性和适应性;瓶颈层多尺度时序模块提高时间建模能力,通过减少通道宽度来节省计算成本和参数。为验证所提方法的有效性,在大型骨架动作识别数据集NTU-RGB+D和NTU-RGB+D 120上进行实验。结果证明,改进后的算法的准确率得到了一定提升。Action recognition methods have achieved significant success in the field of computer vision.Graph convolutional networks(GCNs)are crucial techniques for action recognition tasks,especially for extracting features from graph-structured data.However,existing GCNs suffer from limitations such as an excessive reliance on predefined skeleton topological graphs and a lack of flexibility in handling large temporal convolution kernels,which significantly constrain their expressive power and robustness.In this paper,we propose an adaptive bottleneck multi-scale graph convolutional action recognition method based on skeleton data.The adaptive spatial module optimizes the skeleton topological graph structure and parameters,enhancing the model’s flexibi-lity.The bottleneck layer multi-scale temporal module improves the temporal modeling capabilities while reducing channel width to save computational costs and parameters.Experimental results on large-scale skeleton action recognition datasets,NTU-RGB+D and NTU-RGB+D 120,show that the accuracy of our model is improved to a certain extent.

关 键 词：动作识别 骨架模态 图卷积网络 视频分类 计算机视觉 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]