A Machine Vision Toolkit for Analyzing Tennis Racquet Positioning During Service

The integration of technology into sports, particularly tennis, makes possible new opportunities for performance analysis and skill development. This paper presents a novel approach utilizing image processing and machine learning techniques to find features which are important for analysis of the mechanics of the tennis serve, focusing on the position of the racquet at the point of ball contact. Through temporal averaging and color segmentation, we isolate the player and racquet, facilitating detailed analysis. Additionally, we employ a neural network-based keypoint detection model to accurately identify key points on the tennis racquet. Our methodology enables the extraction of meaningful insights into serve mechanics, providing valuable feedback for players, coaches, and enthusiasts. By democratizing advanced tennis analytics, our research contributes to the enhancement of coaching methodologies and player development. We make our code and models available at https://github.com/Federer123/TennisServeDetection-ML