Automatic Localization of Indoor Soccer Players from Multiple Cameras

Nowadays, there is an ever growing quest for finding sophisticated performance evaluation tools by team sports that could give them an additional inch or a quarter of a second of advantage in a competition. Using cameras to shoot the events of a game, for instance, the teams can analyze the performance of the athletes and even extrapolate the data to obtain semantical information about the behavior of the teams themselves at relatively low costs. In this context, this paper introduces a new approach for better estimating the positions of indoor soccer players using multiple cameras at all moments of a game. The setup consists of four stationary cameras set around the soccer court. Our solution relies on individual object detectors (one per camera) working in the image coordinates and a robust fusion approach working in the world coordinates in a plane that represents the soccer court. The fusion approach relies on a gradient ascent algorithm over a multimodal bidimensional mixture of Gaussians function representing all the players in the soccer court. In the experiments, we show that the proposed solution improves standard object detector approaches and greatly reduces the mean error rate of soccer player detection to a few centimeters with respect to the actual positions of the players.