# An Improved High-Resolution Network-Based Method for Yoga-Pose Estimation
## 1 Introduction  
Two dimensional (2D) human pose estimation has been a hotspot of research in the computer vision field because of its characteristics. Its task is to predict and localize the keypoint parts of a target human body. This is the basis of other action tasks, and 2D human pose estimation has important research and application values. The evaluation of human motion action is receiving more and more attention in the computer vision field, and human pose estimation, as the basis of human action evaluation tasks, has also been the subject of much research, with achieved results. Based on the excellent performance and advancement of deep convolutional neural networks, [PERSON] et al. [1] (2014) introduced deep neural networks into human pose-estimation algorithms for the first time. The model considered the localization of keypoints as a regression problem of human keypoints, extracted image features by convolutional neural networks, and simultaneously modeled the relationship between keypoints using convolutional kernels, which improved the performance of predicting human keypoints. A stacked hourglass network [2] uses a symmetric modular structure from high-resolution downsampling to low-resolution downsampling, followed by low-resolution upsampling to recover the high resolution to fuse the features of different scales to capture multiple spatial relationships. This results in the improvement of the accuracy of the human body keypoints prediction. On this basis, a simple baseline method [3] simplifies the tandem structure from high resolution to low resolution and, then, recovers high resolution, generating high-resolution feature representations from low-resolution feature representations throughthree transposed convolutional layers. The HRNet high-resolution network proposed in 2019 [4] can be distinguished from the previous tandem approach (from high resolution to low resolution, then recovering to high resolution). The HRNet model adopts the parallel connection of high resolution and low resolution to maintain the high-resolution feature map throughout the model, which compensates for the lack of spatial resolution loss generated by upsampling. However, the human body is a non-rigid structure that produces a wide variety of postures and is prone to self-obscuration problems that interfere with the accurate localization of human keypoints. To improve the accurate localization of occluded keypoints, more attention needs to be paid to these regions. Fortunately, model performance can be significantly improved by embedding the attention module into existing CNNs, enabling the network to pay more attention to these key regions. By learning to redistribute the channel weights of convolutional features, the attention mechanism improves model performance, with a slight increase in computational complexity, leading to more accurate prediction of keypoints in the human body. Currently, attention mechanisms such as SENet [5], CBAM [6], and GCNet [7] have achieved sizable performance improvements. Nevertheless, these attention mechanisms can only capture local information, making it difficult to establish long-term dependencies, and they cannot fully access and utilize the spatial information of feature maps at different scales or enrich the feature space. To solve this problem, this paper proposes the squeeze-and-excitation and pixel-attention-mask (SEPAM) module, which utilizes the feature pyramid structure [8] to extract features at different scales using convolution kernels of different sizes. The weights are assigned to different channels by the channel attention (SE) module; then, the pixel positions are assigned by the pixel-attention-mask (PAM) module. Used in parallel, these two attention modules can effectively extract finer-grained multi-scale spatial information, while establishing channel dependencies over longer distances and pinpointing feature information in the spatial direction, thus reducing the loss associated with keypoint localization.