In sports science research, there are many topics that utilize the body motion of athletes extracted by motion capture system, since motion information is valuable data for improving an athlete’s skills. However, one of the unsolved challenges in motion capture is extraction of athletes’ motion information during the actual game or match, as placing markers on athletes is a challenge during game play. In this research, the authors propose a method for acquisition of motion information without attaching a marker, utilizing computer vision technology. In the proposed method, the three-dimensional world joint position of the athlete’s body can be acquired using just two cameras without any visual markers. Furthermore, the athlete’s three-dimensional joint position during game play can also be obtained without complicated preparations. Camera calibration that estimates the projective relationship between three-dimensional world and two-dimensional image spaces is one of the principal processes for the respective three-dimensional image processing, such as three-dimensional reconstruction and three-dimensional tracking. A strong-calibration method, which needs to set up landmarks with known three-dimensional positions, is a common technique. However, as the target space expands, landmark placement becomes increasingly complicated. Although a weak-calibration method does not need known landmarks, the estimation precision depends on the accuracy of the correspondence between image captures. When multiple cameras are arranged sparsely, sufficient detection of corresponding points is difficult. In this research, the authors propose a calibration method that bridges multiple sparsely distributed cameras using mobile camera images. Appropriate spacing was confirmed between the images through comparative experiments evaluating camera calibration accuracy by changing the number of bridging images. Furthermore, the proposed method was applied to multiple capturing experiments in a large-scale space to verify its robustness. As a relevant example, the proposed method was applied to the three-dimensional skeleton estimation of badminton players. Subsequently, a quantitative evaluation was conducted on camera calibration for the three-dimensional skeleton. The reprojection error of each part of the skeletons and standard deviations were approximately 2.72 and 0.81 mm, respectively, confirming that the proposed method was highly accurate when applied to camera calibration. Consequently, a quantitative evaluation was conducted on the proposed calibration method and a calibration method using the coordinates of eight manual points. In conclusion, the proposed method stabilizes calibration accuracy in the vertical direction of the world coordinate system.
Calibration of multiple sparsely distributed cameras using a mobile camera
