A Multiple Hypothesis Approach for a Ball Tracking System

This paper presents a computer vision system for tracking and predicting flying balls in 3-D from a stereo-camera. It pursues a “textbook-style” approach with a robust circle detector and probabilistic models for ball motion and circle detection handled by state-of-theart estimation algorithms. In particular we use a Multiple-Hypotheses Tracker (MHT) with an Unscented Kalman Filter (UKF) for each track, handling multiple flying balls, missing and false detections and track initiation and termination. The system also performs auto-calibration estimating physical parameters (ball radius, gravity relative to camera, air drag) simply from observing some flying balls. This reduces the setup time in a new environment.