## 2 Related Works  
### Pose Estimation Research  
In [8], the field of two-dimensional (2D) human pose estimation research was categorized into Single Person Pose Estimation models and Multi-Person Pose Estimation models. Additionally, each was further divided into regression and heatmap methods,as well as bottom-up and top-down detection methods. Single Person Pose Estimation using the regression method detects joints by regressing joint coordinates directly from the feature map of the image. This method is fast, direct, and trained in an end-to-end fashion. Because of this, it can be applied to three-dimensional (3D) joint estimation without any change. However, it is difficult to train joint positions, and it is not applicable to multiple person pose estimation. BlazePose [9] used the regression method and employed tracking to utilize previous coordinate information for predicting the next coordinates. It is an extended model that can estimate 3D coordinates, obtaining x, y, and z coordinates. However, when using motion as input, the lower the image quality, the more non-detection issues occur in all coordinates. Single Person Pose Estimation using the heatmap method infers joint coordinates through heatmap prediction of the expected joint positions. It is easy to visualize and applicable to complex cases. However, it requires much memory to obtain heatmaps and is difficult to extend to 3D coordinate estimation. HRNet (High-Resolution Network) [10] uses the heatmap method and applies multiple resolutions in parallel during training to learn models that capture both global and local contexts. This model extracted human joint coordinates with higher performance than other models used in the experiments, but issues of false detection and coordinate inversion still existed. Bottom-up coordinate detection for multi-person estimation is a method that estimates the joint coordinates of people in a video and then distinguishes individuals. It detects coordinates quickly by first finding coordinates and then detecting individuals, but it has the disadvantage of lower performance. OpenPose [11], a bottom-up coordinate detection model, distinguishes individuals through features of body parts, thus improving accuracy. However, non-detection issues occurred when using golf swing motion data. Top-down coordinate detection for multiple person estimation is a method that first detects a person and then iteratively estimates the joint coordinates of the single person. It detects more accurate coordinates but is slower since it detects the person first and then iteratively estimates their pose. To improve coordinate estimation performance, both the human detection and detected human pose estimation parts need refinement. Geometric and spatial transformation processes using STN (Spatial Transformation Network) and SDTN (Spatial Detransformer Network) were suggested in [12]. This network [12] extracts high-quality human candidate frames and shows features that improve recognition performance. Moreover, ref. [13], currently a state-of-art model, significantly improved pose estimation performance through the Vision transformer. However, the current model size ranges from 1 million to 1 billion, which requires high computational costs.