Development of a Monocular Camera-Based Sweep Analysis System in Curling

Curling requires precise sweeping to control both the speed and trajectory of a stone, making quantitative analysis of sweeping behavior an important challenge for performance evaluation and tactical support. However, existing curling studies have primarily focused on stone motion analysis, while automatic estimation of sweep positions and brush orientations from monocular video remains largely unexplored. To address this problem, this study proposes a vision-based system for estimating the position and orientation of curling brushes during sweeping using a single RGB camera. The proposed framework integrates a two-stage YOLOv8-OBB detection pipeline for identifying sweeping players and brush regions, ByteTrack-based multi-object tracking for temporal association, and a geometric calibration model that transforms image coordinates into real-world curling-sheet coordinates through nonlinear optimization. The system enables frame-by-frame reconstruction of sweeping trajectories and brush orientations without requiring specialized sensing equipment. Experiments were conducted at a curling facility using synchronized monocular video and a Qualisys Miqus M3 motion-capture system as ground truth. The proposed method achieved position estimation errors of 0.03–0.06 m and orientation estimation errors of 4.80°–5.10°, while maintaining a high detection rate of 0.96–0.99 over a measurement range of 15 m. Additional analyses showed that estimation accuracy was influenced by camera placement, sweep style, and the apparent size of the detected brush region. These results demonstrate that accurate sweep-position and orientation estimation can be achieved using only a monocular camera under realistic curling conditions. The proposed system provides a practical framework for objective sweep analysis and has potential applications in technical training, performance assessment, and tactical support in curling.