Research on tennis motion prediction and landing point estimation based on computer vision and kinematic models

Addressing trajectory prediction errors caused by high-speed spin and airflow interference in tennis, this study proposes a motion prediction and landing point estimation method integrating computer vision with dynamic modeling. Initial parameters are obtained through multi-camera visual localization, combined with air resistance and the Magnus effect to construct dynamic equations, and numerical integration is employed for trajectory prediction. Experimental results demonstrate that the average landing point error under complex spin and crosswind conditions is less than 0.15 m, with significantly improved prediction stability compared to single-model approaches. This research provides high-precision computational support for intelligent tennis training and motion analysis.