Tracking the trajectory of tennis players can help camera operators in production. Predicting future movement enables cameras to automatically track and predict a player's future trajectory without human intervention. Predicting future human movement in the context of complex physical tasks is also intellectually satisfying. Swift advancements in sports analytics and the wide availability of videos for tennis have inspired us to propose a novel method called Pose2Trajectory, which predicts a tennis player's future trajectory as a sequence derived from their body joints' data and ball position. Demonstrating impressive accuracy, our approach capitalizes on body joint information to provide a comprehensive understanding of the human body's geometry and motion, thereby enhancing the prediction of the player's trajectory. We use encoder-decoder Transformer architecture trained on the joints and trajectory information of the players with ball positions. The predicted sequence can provide information to help close-up cameras to keep tracking the tennis player, following centroid coordinates. We generate a high-quality dataset from multiple videos to assist tennis player movement prediction using object detection and human pose estimation methods. It contains bounding boxes and joint information for tennis players and ball positions in singles tennis games. Our method shows promising results in predicting the tennis player's movement trajectory with different sequence prediction lengths using the joints and trajectory information with the ball position.

本研究解决了网球运动员轨迹预测的挑战，提出了名为Pose2Trajectory的新方法，通过分析身体关节数据和球的位置来预测运动员的未来轨迹。该方法利用变换器编码-解码架构，实现了高精度的预测，有助于自动追踪运动员，提高了运动分析和摄像机操控的效率。

Pose2Trajectory: 使用变换器通过身体姿态预测网球运动员的轨迹