balltracking

Balltracking is a domain of computer vision and sports analytics concerned with detecting and following a spherical object, commonly a ball, as it moves through video frames. The objective is to determine the ball’s position in each frame and, when possible, to estimate its trajectory in two or three dimensions.

Typical approaches start with detection, using color or brightness cues, background subtraction, circular shape detectors such as the Hough circle transform, or machine learning-based detectors that classify image regions as ball or not. Once detections are obtained, tracking mechanisms link detections across frames. Common techniques include Kalman filters, extended Kalman filters, or particle filters to predict motion, along with data association methods like the Hungarian algorithm to maintain consistent ball tracks. In multi-camera setups, triangulation and 3D reconstruction can yield the ball’s spatial path.

Balltracking is widely used in sports analytics to analyze ball speed, spin, and trajectory in football, tennis, cricket, basketball, and baseball. It supports broadcast visualization, coaching insights, and performance research, as well as automated highlight generation.

Challenges include occlusion by players, rapid motion and motion blur, changing lighting and shadows, and variations in ball appearance across sports and camera angles. Accurate tracking often requires robust detectors, high frame rates, and camera calibration.

Evaluation measures focus on track accuracy, position error, trajectory smoothness, and robustness under occlusion. See also object tracking, motion estimation, and Hough transform.