Field-based 3D kinematic analyses from a stereo camera setup for football

Accurate measurement of lower-limb kinematics in outdoor sports is challenging due to the limitations of marker-based motion capture and inertial sensors. This study evaluates a stereo camera–based 3D pose estimation pipeline using real-time multi-person one-stage (RTMO) detection to reconstruct sagittal knee and hip angles during running, sprinting and jumping. Twelve adult football players were recorded with two synchronised GoPro cameras, while inertial measurement units (IMUs) provided reference joint angles. RTMO extracted 2D keypoints from both views, which were triangulated to 3D joint positions for angle computation. During running at 2–4 m, agreement with IMUs was strong (r > 0.90, RMSE < 10°). At 8 m, accuracy declined (RMSE 23.74°, r = 0.80). Sprinting produced higher errors than running and jumping yielded the lowest error (RMSE 5.11°, r = 0.98). Findings indicate that stereo-based pose estimation can capture joint kinematics outdoors, with accuracy dependent on distance and activity type.