Camera Selection for Broadcasting Soccer Games

Abstract

When broadcasting events such as soccer games, human operators constantly select the camera with the best viewpoint to cover the whole event. Modeling the prediction of which camera should be on air will assist automatic sports broadcasts and influence millions of viewers. In this paper, we propose a proof-of-concept method to automatically select cameras for broadcasting soccer games. First, a random forest based regressor smoothly predicts the visual importance of short video clips using deep convolutional features. Then, the predictions from multiple candidate cameras are regularized by a novel camera duration cumulative distribution function (CDF), naturally guiding the camera selection. We apply our approach to real soccer broadcasts with a professional human operator's result as a reference. The quantitative experiments demonstrate that our method outperforms two alternatives in terms of prediction accuracy. Moreover, the video generated by our method is preferred in the user study experiment, exhibiting its practicality.