Human pose estimation has a wide range of applications. Existing methods perform well in conventional domains, but there are certain defects when they are applied to sports activities. The first is lack of estimation of the extremity posture, making it impossible to comprehensively evaluate the movement posture; the second is insufficient occlusion handling. Therefore, we propose a human pose compensation network based on incremental learning, which obtains shared weights to extract detailed features under the premise of limited extremity training data. We propose a higher-order feature compensator (HOF-compensator) to embed the attributes of the extremity into the torso and limbs topology structure, building a complete higher-order feature. In addition, to improve the occlusion handling performance, we propose an occlusion feature enhancement attention mechanism (OFE-attention) that can identify occluded keypoints and enhance attention to occlusion areas. We design comparative experiments on three public datasets and a self-built sports dataset, achieving the highest mean accuracy among all comparative methods. In addition, we design a series of ablation analysis and visualization displays to verify that our method performs best in sports pose estimation.

人体姿态估计具有广泛的应用。现有方法在传统领域表现良好，但在应用于体育活动时存在一定的缺陷。一是缺乏对四肢姿势的估计，无法全面评价运动姿势;第二个是遮挡处理不足。因此，我们提出了一种基于增量学习的人体姿态补偿网络，在有限的肢体训练数据的前提下，获取共享权重来提取详细特征。我们提出了一种高阶特征补偿器（HOF-compensator），将肢体的属性嵌入到躯干和肢体拓扑结构中，构建一个完整的高阶特征。此外，为了提高遮挡处理性能，我们提出了一种遮挡特征增强注意力机制 （OFE-attention），可以识别被遮挡的关键点并增强对遮挡区域的关注。我们在三个公共数据集和一个自建运动数据集上设计了比较实验，在所有比较方法中取得了最高的平均准确率。此外，我们还设计了一系列消融分析和可视化显示，以验证我们的方法在运动姿势估计中表现最佳。