基于多尺度双注意力的人体姿态估计方法研究

Study on Human Pose Estimation Based on Multiscale Dual Attention

针对人体姿态估计中人体与背景区分度不高,基于HRNet网络的人体姿态估计中重要特征信息利用不完全的问题,利用通道与空间注意力机制,提出了一种基于多尺度双注意力(Multiscale Dual Attention,MDA)的人体姿态估计方法MDAHRNet。该方法从通道域和空间域出发,分别设计了结合通道注意力的Ca-Neck,Ca-Block模块和结合空间注意力的Sa-Block模块,将其融入到高分辨率网络结构中,使网络能够重点关注图像中的人体区域。在Sa-Block模块中采用3×3和7×7的卷积核推导两种不同尺度的空间注意力映射,使网络区分人体特征和背景特征的能力更加显著,从而对人体及其关键点进行准确定位。该方法在MPII数据集上进行了实验验证,结果表明MDA-HRNet能有效地提高人体姿态估计关节点定位的准确度。

In view of the problem of low discrimination between human body and background in human posture estimation,and incomplete utilization of important feature information in human posture estimation based on HRNet,a human posture estimation method MDA-HRNet based on multiscale dual attention is proposed by using channel and spatial attention mechanism.Considering both of the channel domain and spatial domain,the Ca-Neck and Ca-Block modules combined with channel attention and SaBlock module combined with spatial attention are designed respectively.Then integrating these modules into the high-resolution network structure,so that the network can pay more attention to the human body area in the image.Moreover,in the Sa-Block module,3×3and 7×7convolution kernels are adopted to derive two spatial attention maps of different scales,which makes the ability of the network to comprehensively distinguish human features and background features more remarkable,so as to accurately locate the human body and its key points.The proposed method is tested and verified on MPII data set,and the results show that MDA-HRNet can improve the accuracy of joint point location of human posture estimation effectively.