We developed a system for visualizing stone trajectories in curling games for live broadcasts. Robustly tracking a moving stone from curling video sequences is difficult because the stone is frequently hidden by the brushes held by the players and the players’ bodies during their sweeping actions. We thus propose an online machine learning method for tracking a curling stone to deal with changes in its appearance. The method creates a candidate-object image, which eliminates background noises, and is used as input to the kernelized correlation filter (KCF) tracker. The developed system was used at All Japan Curling Championships 2017 to display stone trajectories during live broadcasts.
