A Robust Intelligent Framework for Long Jump Action Scoring: From Pose Estimation to Motion Blur-Resistant Recognition

With the advancement of deep learning, sports motion analysis has become in-creasingly data-driven. However, techniques such as pose estimation, action recognition, and scoring often operate independently. To address this limitation, a unified framework is proposed for structured and objective long jump analysis. One major challenge in real-world scenarios is motion blur, which greatly reduc-es the accuracy of pose estimation. To mitigate this issue, a long jump dataset was collected from 30 athletes, annotated across four movement phases, multiple lighting conditions, and four levels of motion blur. Based on this dataset, a simple MetaFormer-based model named BaseFormerPose is developed, using uniformly stacked window self-attention. It achieves 91.0 AP on the long jump motion-blur dataset. An automatic scoring module is also introduced, and its outputs show strong agreement with pose-based scores from three expert coaches, suggesting improved consistency and reduced subjectivity in long jump evaluation.