Feature Extraction Algorithm of Basketball Trajectory Based on the Background Difference Method

People are increasingly interested in moving object detection technology as a part of computer vision technology, which has grown in importance in recent years. Moving target detection technology is widely employed in military defense, security monitoring, medical examinations, intelligent transportation, and many other industries at the present time. A moving target detection system relies on rapid and accurate video picture segmentation to identify, locate, and analyze the target. The trajectory of a basketball player’s shooting motion and the extraction of features are important to increase the accuracy of basketball players’ shots. This study proposes an area growth algorithm-based approach to tracing the shooting motion of basketball players. The trajectory of the shot motion is captured using a video sensor image tracking approach. The corner points and the edge contours of the trajectory image are extracted using the edge contour feature extraction method, the feature extraction from the firing motion trajectory image is merged with the regional linear growth approach, and the corner points are then marked. Feature extraction improves basketball players’ shooting accuracy and trajectory control ability. Simulation results show that the prediction accuracy of basketball players’ shooting trajectories using this method can reach up to 100%, which improves the accuracy of motion trajectory extraction and enhances basketball players’ shooting motion control.