Hard-joint localization in human pose estimation is a challenging task for some reasons, such as the disappearance of joint points caused by clothing and lighting, the shelter caused by complex environment and the destruction of dependence among each joint point. A majority of existing approaches for hard-joint pose estimation achieve high accuracy by obtaining more high-level feature information. However, most networks suffer from information loss, which is caused by down-sampling. This would result in the loss of joint location. The compensation of information loss introduces useless information to network learning, affecting the extraction of useful information associated with hard joints. Herein, a residual down-sampling module is proposed to replace the pooling layer for down-sampling and fuse high-level features with low-resolution feature maps. This module aims to address the information loss issue. A strategy to guide network learning based on the attention mechanism is proposed, which makes the network focus on useful feature information. A convolutional block attention module is combined with a residual module outside the basic sub-network. The network can learn more effective high-level features. An eight-stack hourglass is used as the basic network, and the proposed method is validated on the MPII and LSP Human Pose dataset. Compared with eight-stack hourglass and HRNet, the proposed method achieves higher accuracy for hard-joint localization. The experimental results show our proposed methods effective for hard-joint localization.

由于某些原因，人体姿势估计中的硬关节定位是一项具有挑战性的任务，例如，衣服和灯光导致的关节点消失，复杂环境导致的庇护所以及每个关节点之间的依存关系遭到破坏。现有的大多数用于硬关节姿势估计的方法都是通过获取更多高级特征信息来实现的。但是，大多数网络都遭受信息丢失的困扰，这是由下采样引起的。这将导致关节位置的损失。信息丢失的补偿将无用的信息引入到网络学习中，从而影响了与硬关节相关的有用信息的提取。在此处，提出了一个剩余的下采样模块，以代替用于下采样的池化层，并融合具有低分辨率特征图的高级特征。该模块旨在解决信息丢失问题。提出了一种基于注意力机制的网络学习策略，使网络关注有用的特征信息。卷积块注意模块与基本子网外部的残差模块组合。该网络可以学习更有效的高级功能。以八层沙漏为基本网络，并在MPII和LSP Human Pose数据集上验证了该方法的有效性。与八层沙漏和HRNet相比，该方法对硬关节定位具有更高的精度。实验结果表明，我们提出的方法可以有效地进行硬关节定位。