# Shooting Prediction Based on Vision Sensors and Trajectory Learning
## 1 Introduction
For lower limb recognition of basketball poses, the recognition accuracy of random forest algorithm reaches 94.1%. Regarding the prediction of shots, the most instructive method is [PERSON]'s method [16]. He used the frame difference method to process the shooting video images, and obtained the basketball trajectory from the shooting start point to the shooting end point. After that, he used basic mechanics theory to analyze and calculate the relationship between shooting angle and shooting speed, which provided an effective trajectory prediction method and reference for obtaining shooting percentage. The authors highlight some of the factors that have an impact on shot success. These factors include shot height, shot speed, shot angle, and potential wind speed that may be present. Through experiments, this paper illustrates the effects of different wind directions, wind speeds, shooting angles, shooting distances, and heights on the shooting percentage. [PERSON] [17] designed an automatic shooting robot. He improved the shooting percentage through guiding the robot to adjust the shooting angle by means of a laser pointer. In the laboratory, the robot's shooting accuracy reached almost 90%. However, performance may depend on the lighting conditions in the shooting environment. [PERSON]. [18] analyzed the shooting data of NBA players and made shot predictions using two methods including random forest and XGBoost. The shooting prediction rate can reach 68%. Whether it is an emotional factor, a slight loss of balance, or just a small movement out of place, it can affect the outcome of a shot. Although there have been more and more research works related to basketball players' shooting performance in recent years, there are very few studies on trajectory prediction using the information and data reflected from the set of shooting trajectories. The study found that it is possible to predict information about shooting through the early performance information of basketball trajectory changes. This paper attempts to use these data to analyze and predict basketball percentages. The object of our study is free-throw shooting. Therefore, we specify that the player needs to stand at the free-throw line to make the shot. In this way, we ensure that the player's position with respect to the basket is fixed, and the player faces towards the basket. The shooting trajectory can be considered to be in a two-dimensional plane since there is little motion in the third dimension. Therefore, we can determine a two-dimensional plane from the player to the basket. The features are extracted through the two-dimensional plane. A reasonable shot trajectory will increase the probability of the basketball going into the basket and a greater chance of scoring. Especially the farther away from the basket, the more the athlete should consider the trajectory of the shot. The accurate movement trajectory can help players adjust their shooting posture and strength to improve their free-throw shooting performance. Based on the trajectory learning prediction model, this paper provides a novel and effective method for the instantaneous prediction of shooting results. Collecting a large number of shooting videos, the template matching method and the regional centroid method are used to obtain the motion trajectory coordinates of the basketball in the video. At the same time, a variety of different trajectory motion feature combinations are used to construct feature combinations, and the instantaneous prediction model is trained by the trajectory learning method. Finally, according to the early basketball movement characteristic data after the basketball is shot, the shooting is judged. The contributions of this paper are as follows:  
1. A new method using trajectory learning is proposed; we use the early trajectory motion data of basketball trajectories to achieve shot prediction;
2.