LSTM-BEND: Predicting the Trajectories of Basketball

The capacity to comprehend and anticipate the movements of dynamic objects deployed in sports contexts is a need for autonomous systems. A basketball game is an example of one such ambiance, with its dynamic and intricate motions fueled by numerous social interactions. One such crucial part of such systems is to forecast future basketball positions to determine whether or not the ball will hit or miss the basket based on those predictions. This is a difficult task as basketball motion depends on several factors, including the stroke applied by the player on the ball, environmental factors like topology and geometry, etc., and the fact that the subject of analyzing the trajectory of the basketball is one which is interesting interested. In this letter, we concentrate on predicting basketball motion trajectories in actual basketball tournaments, and the objective is to forecast basketball's future locations based on its past positions. Therefore, this letter investigates whether long short-term memory models can accurately anticipate the motion trajectories of basketballs in light of their performance in sequence prediction challenges. In particular, a long-short-term memory network-integrated relation network-based bimodal exponential normal distribution process is suggested.