【摘要】 针对现有图卷积网络(GCN)需要预先定义人体骨架拓扑图和模型较大的问题，提出了基于时空自适应图卷积网络(ST-AGCN)的跌倒检测算法。该网络包括3个部分：利用HRNet姿态估计算法从视频中提取人体骨架点序列，并预处理成四维张量；引入归一化嵌入式高斯函数通过学习(无需人工预定义)得到人体拓扑图，利用空间自适应图卷积获取人体关联特征；利用多尺度卷积提取时间运动特征，提高模型获取动态信息的能力。在公开数据集和自建数据集上分别进行仿真，准确率分别达95.45%和99.55%。结果表明，该算法优于目前GCN方法，参数量只有后者的1/4甚至更少。本文算法还可以适用于不同的数据集。更多还原

【Abstract】 To solve the problem that existing graph convolution network(GCN) need to pre-define human skeleton topology and the model is large, a fall detection algorithm based on spatiotemporal adaptive graph convolutional network(ST-AGCN) is proposed. The network consists of three parts: firstly, HRNet, a human pose estimation algorithm, is used to extract human skeleton points from video and preprocess them into four-dimensional tensor. Secondly, the normalized embedded Gaussian function is introduced to obtain the human body topology by learning(without manual pre-definition), and the human body correlation features are obtained by spatial adaptive graph convolution. Thirdly, multi-scale convolution is used to extract temporal motion features to improve the model′s ability to obtain dynamic information. Simulations are carried out on public and self-built dataset, and the accuracy rates are 95.45% and 99.55%, respectively. The results show that the proposed algorithm is better than the current GCN methods, and the number of parameters is only a quarter of the latter, or even less. Another advantage of our algorithm is that it can be applied to different datasets.更多还原