# Exploration of Applying Pose Estimation Techniques in Table Tennis
## 2 Literature Review
### Posture Estimation during Movement  
Using posture estimation techniques in sports remains an emerging research area. Deep learning techniques for human activity recognition are a challenging task in computer vision [17; 18]. The advancement of technology enables the application of deep learning techniques to real-time motion pose detection through pose estimation in computer vision [19]. To apply deep learning to sports, scholars have used a hybrid deep learning architecture that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network for yoga posture recognition [20], pingpong ball drop detection, and billiard ball drop detection [21]. Body sensor networks (BSNs) are used to collect motion data, and sensors are placed on the upper arm, lower arm, and back to collect the above motion information. Then, dimension reduction by principal component analysis, and finally, a support vector machine (SVM) for machine learning, were used to analyze the table tennis strokes detection [3]; the SVM analyzed the table tennis strokes videos. A 240 fps image was captured with an RGB high-speed camera, which was reduced to 30 fps to accelerate the stance and ball estimations. The stance estimation was performed with a residual CNN network to estimate the player's body stance. The LSTM model predicted the ball drop using only 10 joint positions in two dimensions of the upper body [19].  
In addition to body posture estimation, most current literature on computer vision applications in table tennis focuses on applying racket posture estimation to table tennis robots. Previous research has proposed the detection of the image of the racket area during the motion of a table tennis ball with a modified LineSegmentDector (LSD) algorithm to analyze the vertices and rectangular areas of the racket image, and, finally, integrate the center and pose of the 3D racket area to obtain the pose and position of the racket using a PnP positioning method to implement the application in a table tennis robot [22]. [PERSON] et al. [23] used a high-speed monocular vision system to track the trajectory of the racket to estimate the ball rotation and proposed a novel and effective feature filtering method to screen out important features of the racket's posture during a collision. A new combined HSV and RGB color space algorithm was used to estimate the racquet stance  
Figure 1: OpenPose detects the key points of body posture, facial expression, hands, and legs of multiple people at the same time. Photo credits: [[https://arxiv.org/pdf/1812.08008.pdf](https://arxiv.org/pdf/1812.08008.pdf)]([https://arxiv.org/pdf/1812.08008.pdf](https://arxiv.org/pdf/1812.08008.pdf)) (access date: 1 October 2022).  
using real-time racquet image detection [24]. The estimation of the image's rectangular area was shown to be effective. Recently, some scholars [21] used RGB camera images to analyze the sender's motions through a long short-term pose prediction network to accurately predict the landing point of the serve [18]. To enhance the learning of video features, a topological sparse encoder was constructed for semi-supervised learning, which could effectively enhance the application of computer vision technology to table tennis video pose recognition.