The use of computer vision technology to collect and analyze statistics during badminton matches or training sessions can be expected to provide valuable information to help coaches to determine which tactics should b...The use of computer vision technology to collect and analyze statistics during badminton matches or training sessions can be expected to provide valuable information to help coaches to determine which tactics should be used by a player in a given game or to improve the player＇s tactical training. A method based on 2-D seriate images by which statistical data of a badminton match can be obtained is presented. Image capture and analysis were performed synchronously using a multithreading technique. The regions of movement in the images were detected using a temporal difference method, and the trajectories of the movement regions were analyzed using sedate images. The shuttlecock trajectory was extracted from all detected trajectories using various characteristic parameters. The stroke type was determined by comparing the shuttlecock trajectory data with a set of stroke definition data. The algorithm was tested at a training center, and the results were compared with baseline data obtained by expert visual inspection using four video samples, which included approximately 10 000 frames. The shuttlecock trajectory and stroke type were detected correctly in almost 100% of the analyzed video sequences. The average speed of the automated analysis was approximately 40 frames/s, indicating that the method can be used for real-time analysis during a badminton match. The system is convenient for use by a sports coach.展开更多
文摘The use of computer vision technology to collect and analyze statistics during badminton matches or training sessions can be expected to provide valuable information to help coaches to determine which tactics should be used by a player in a given game or to improve the player＇s tactical training. A method based on 2-D seriate images by which statistical data of a badminton match can be obtained is presented. Image capture and analysis were performed synchronously using a multithreading technique. The regions of movement in the images were detected using a temporal difference method, and the trajectories of the movement regions were analyzed using sedate images. The shuttlecock trajectory was extracted from all detected trajectories using various characteristic parameters. The stroke type was determined by comparing the shuttlecock trajectory data with a set of stroke definition data. The algorithm was tested at a training center, and the results were compared with baseline data obtained by expert visual inspection using four video samples, which included approximately 10 000 frames. The shuttlecock trajectory and stroke type were detected correctly in almost 100% of the analyzed video sequences. The average speed of the automated analysis was approximately 40 frames/s, indicating that the method can be used for real-time analysis during a badminton match. The system is convenient for use by a sports coach.
