# Shooting Prediction Based on Vision Sensors and Trajectory Learning
## 1 Introduction  
The development of artificial intelligence technology has led to changes in real life and promoted progress in various fields, and the sports industry is no exception [1]. Artificial intelligence technology is powerfully changing sports, and has greatly developed in medical rehabilitation, physical therapy, conditioning training, exercise guidance, etc. [2; 3; 4; 5; 6]. Basketball is a popular sport, and the research and application of artificial intelligence and ML in play guidance and sports analysis is very extensive. Artificial intelligence technology allows basketball players to get immediate and long-term feedback in training and let them know how perfectly their movements are being executed. Furthermore, it is also used by coaches to provide reinforcement feedback, which is one of the most powerful factors influencing the motor learning and ability of basketball players [7]. In basketball, shooting is arguably the most important technique, and shooting pattern is the basic indicator for evaluating player performance in professional basketball research. A good shooter can achieve precision shooting from distance, position within the standard area, or available power to avoid blocking shots from close range, which is why shooting technique has been the subject of much research over the years. For example, [PERSON] [8]selected nine players and analyzed the shooting patterns of basketball players in the form of visual charts. To obtain better metrics of basketball performance, recent research efforts have turned to machine learning of wearables data using AI [9; 10]. The current research mostly stays on the identification and analysis of the shooting action, and there are few types of research on the shooting percentage prediction. From the point of view of the key equipment used, the research on the recognition and analysis of shooting movements has formed two common ways, namely wearable sensors and video. There are few studies based on wearables. For example, [PERSON] [11] collects shooting accelerometer data, analyzes its characteristics, and uses machine learning to build a shooting motion analysis system, which can provide a reference for athletes' shooting motions. It shows that four different shot types can be classified with an accuracy of 86.3% using a Quadratic SVM classifier. However, this approach requires more experimentation and more complex characterization. [PERSON] [12] used the smart wristband embedded with sensors to analyze 18 shooting movements and identify the shooting movements. [PERSON] [13] developed a smart device using an IMU and WIFI module, which can record and recognize the shooting action. The device described in the paper can be adapted to an individual's shooting movements and provide accurate real-time improvements during training. However, this method can only judge free throws standing on the free-throw line, and it lacks consideration of other positions. In recent years, shooting action through the analysis of shooting videos has become a hot issue in the field of sports analysis, which can provide assistance for standardizing athletes' movements and improving the effectiveness of basic training. Obtaining the basketball trajectory and analyzing the motion characteristics of the trajectory is the basis for predicting the shooting result. In addition, other researchers have used image processing technology and artificial intelligence technology to analyze players' shooting movements [14; 15]. [PERSON] proposed a basketball shooting gesture recognition method based on image feature extraction and machine learning. The author mainly uses the method of image feature extraction to collect the action pose data of basketball players and extracts multi-dimensional action pose features of the time domain and frequency domain. After that, the authors achieved accurate classification and recognition of basketball shooting gestures. For upper limb recognition of basketball gestures, the recognition accuracy of the Bayesian algorithm can reach 91.2%.