Basketball self training shooting posture recognition and trajectory estimation using computer vision and Kalman filter

Self-shooting training is one of the fundamental criteria for success in basketball. Particularly, young players increase their performance with regular training. However, the training process becomes painful and time-consuming without a coach since the incorrect shooting posture causes missing shots, leading to reluctance. In this research, a self-shooting posture algorithm is developed to track the movement of basketball players and give them feedback about their position, angle, and basketball projectile trajectory information. The proposed algorithm uses computer vision techniques and Kalman filter to detect the best projectile trajectory using initial conditions such as acceleration due to gravity the initial velocity at the angle of launch having certain horizontal distance to the rim and the rim distance from the ground The acceleration of both gravity and air drag are altered by predefined parameters, including the drag coefficient basketball mass ball radius and silhouette area The proposed algorithm provides the shooting angle in real-time by placing the projectile angle on to the cropped image of the player posture and draws the projectile trajectory towards the basketball hoop According to the results, the players having a specified height can achieve the best shooting at the angle with air drag force. On the other hand, if there is no air resistance, the best shooting angle is deviated significantly. The other stats that are a total time of travel, maximum horizontal distance, maximum height and the time until the top are also given along with the results.