Ball Tracking in Sports with Computer Vision

Ball tracking in sports, such as soccer and basketball, presents significant challenges for AI systems due to factors like the ball's small size, high velocity, complex backgrounds, similar-looking objects, and varying lighting conditions. Addressing these challenges involves training a model using a suitable dataset, such as the one from the DFL - Bundesliga Data Shootout Kaggle competition, and employing techniques like image slicing to improve detection accuracy. The process includes training a YOLOv8x model, preprocessing video frames, and using InferenceSlicer to handle small object detection by dividing images into smaller patches. A simple tracking system is implemented to filter anomalies by averaging the ball's position over frames, while visualization techniques such as TriangleAnnotator and BallAnnotator help illustrate the ball's movement. This foundational system can be expanded with advanced tracking algorithms and visualizations to enhance sports AI applications, as demonstrated in the tutorial.