# An Improved High-Resolution Network-Based Method for Yoga-Pose Estimation
## 2 Related Work  
### Human Posture-Estimation Model  
Before introducing 2D human pose-estimation methods by deep learning, the human pose problem was mainly solved using methods based on graph structure models [9; 10]. However, these methods are greatly affected by factors such as figure occlusion, shooting angle, and image illumination, with limited representation capability and poor model prediction accuracy, which is challenging in meeting the needs of practical applications. With the continuous development of deep-learning network models, the performance of human posture estimation has been significantly improved. This paper briefly reviews the deep-learning-based pose estimation methods in recent years. Deep-learning-based 2D human pose =estimation methods can be categorized into top-down and bottom-up architectures. The top-down architecture first detects a single-person image region using an excellent human target detector [11; 12], then uses a human pose estimator [2; 3; 4; 13] to localize the human keypoints in each box. In contrast, the bottom-up human pose-estimation method [14; 15; 16; 17] first detects all the human keypoints in the image, then obtains the pose of each person by matching. The bottom-up method has faster operation speed and higher real-time performance thano the top-down method, but it is prone to errors for the same types of keypoints that are close to each other. Two-dimensional human pose estimation [2; 3; 4; 17; 18; 19; 20] has dominated the field of human pose estimation in terms of performance. Several studies [4; 18; 19; 20; 21; 22; 23] constructed new pose estimation network architectures to extract better features. The creation of the HRNet [4; 16] family of models was a notable development in the area of pose recognition. The HRNet model employed a new convolutional neural network (CNN) architecture designed to model high-resolution feature responses, demonstrating the use of the 2D human pose estimation. YOLO-Pose [18] performed keypoint regression and grouping by utilizing the centroid of the bounding box for yolo target detection as the initial point to achieve accurate localization of the keypoints. TDMI [22] proposed a mutual information-based time-difference learning model that utilized the mutual information to direct the model to learn the features relevant to the task, improving the performance of human pose estimation via video. PoseIG [23] designed attribute-based metrics to assist in analyzing and diagnosing pose-estimation frameworks. Other studies [24; 25; 26; 27; 28; 29] built on the optimization perspective and tried to reduce the inference latency of pose estimation by lightweighting the modules or reducing the network branches, but often at the cost of drastically reducing the accuracy of pose estimation. The optimization perspective also includes studies that employed knowledge distillation methods [30; 31; 32] by constructing lightweight mini-models and using supervisory information from larger models with better performance to train the mini-models, with a view to obtaining better performance and accuracy. For example, OKDHP [30] proposed an online positional distillation method to extract positional structure knowledge in a single-stage approach. DistilPose [32] proposed a heatmap-regression distillation framework to achieve knowledge transfer between heatmap-based and regression methods. In recent research, Transformer has achieved great success in the field of natural language processing (NLP). In the field of 2D human pose estimation, many studies [19; 20; 33; 34; 35; 36] have also incorporated Transformer structures. TFPose [33] first introduced Transformer into a pose estimation framework in the form of regression; PRTR [35] proposed a two-stage end-to-end regression based on a cascaded Transformer framework that achieved excellent performance in regression-based approaches; TransPose [20] and TokenPose [19] introduced Transformer for heat map-based human pose estimation and achieved comparable performance.