Enhancing hurdles athletes’ performance analysis: A comparative study of cnn-based pose estimation frameworks

Abstract Human pose estimation has gained significant attention in recent years for its potential to revolutionize athletic performance analysis, enhance understanding of player interactions, and optimize training regimes. Deep learning models, particularly Convolutional Neural Networks (CNNs), have outperformed traditional methods in pose estimation tasks. This study addresses a gap in sports analytics by applying two popular CNN-based frameworks, YOLO and DeepLabCut, to analyze pose estimation in hurdles athletes. Videos of a single female athlete during training sessions were used, and frames were manually annotated to capture three critical foot landmarks: ankle, heel, and big toe. The results highlight YOLOv8l’s superior accuracy, achieving a Percentage of Correct Keypoints (PCK) of 79% for these landmarks, while demonstrating the feasibility of a low-cost setup for practical applications. Visual comparisons further validate the model’s effectiveness in real-world scenarios. Additionally, YOLO predictions were utilized to analyze step progression in the time domain, providing actionable insights into athletic movement. This study underscores that even modest video equipment, combined with CNN-based methods, can equip coaches with powerful tools to analyze and optimize movements and techniques, paving the way for data-driven advancements in sports performance.