Real-Time Modeling of 3-D Soccer Ball Trajectories From Multiple Fixed Cameras

Abstract

In this paper, model-based approaches for real-time 3-D soccer ball tracking are proposed, using image sequences from multiple fixed cameras as input. The main challenges include filtering false alarms, tracking through missing observations, and estimating 3-D positions from single or multiple cameras. The key innovations are: 1. incorporating motion cues and temporal hysteresis thresholding in ball detection; 2. modeling each ball trajectory as curve segments in successive virtual vertical planes so that the 3-D position of the ball can be determined from a single camera view; and 3. introducing four motion phases (rolling, flying, in possession, and out of play) and employing phase-specific models to estimate ball trajectories which enables high-level semantics applied in low-level tracking. In addition, unreliable or missing ball observations are recovered using spatio-temporal constraints and temporal filtering. The system accuracy and robustness are evaluated by comparing the estimated ball positions and phases with manual ground-truth data of real soccer sequences.