A computational framework for sports analytics: Player tracking and strider rate estimation using deep learning

This paper presents a computational framework for estimating stride rate in football using advanced computer vision and deep learning techniques, integrating modules for player detection, tracking, team identification, pitch mapping, and performance analysis. Convolutional Neural Networks (CNNs) are used for spatial feature extraction, while YOLOv۸ enables accurate player detection, and a Kalman filter supports robust multi-object tracking by modeling player motion as continuous trajectories in two-dimensional Euclidean space. Player kinematics are derived by computing velocity as the time derivative of position and total distance as the cumulative displacement between frames. The system incorporates AlphaPose for anatomical keypoint detection, allowing precise motion capture, and models periodic movement using sinusoidal functions to estimate stride frequency. To enhance accuracy, the Savitzky–Golay filter is applied for trajectory smoothing. Experimental evaluation on broadcast football footage demonstrates strong performance, achieving ۹۳.۱% consistency in stride rate estimation, a ۹۰.۳% success rate, and an error margin below ۲%. Additionally, the integration of digital twinning technology enables real-time visualization of player movements, supporting applications in performance optimization, fatigue monitoring, and injury prevention, thereby advancing automated sports analytics through data-driven decision-making.

کلیدواژه ها:

Stride Rate Estimation ، Kalman filter ، convolutional neural networks ، Biomechanical Gait Modelling ، Euclidean Motion Trajectory