School of Electronics and Information Engineering, Anhui University, Hefei, 230601, Anhui, China.
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, Anhui, China.
Sci Rep. 2023 Apr 15;13(1):6132. doi: 10.1038/s41598-023-32893-x.
Ski jumping is a high-speed sport, which makes it difficult to accurately analyze the technical motion in a subjective way. To solve this problem, we propose an image-based pose estimation method for analyzing the motion of ski jumpers. First, an image keypoint dataset of ski jumpers (KDSJ) was constructed. Next, in order to improve the precision of ski jumper pose estimation, an efficient channel attention (ECA) module was embedded in the residual structures of a high-resolution network (HRNet) to fuse more useful feature information. At the training stage, we used a transfer learning method which involved pre-training on the Common Objection in Context (COCO2017) to obtain feature knowledge from the COCO2017 for using in the task of ski jumper pose estimation. Finally, the detected keypoints of the ski jumpers were used to analyze the motion characteristics, using hip and knee angles over time (frames) as an example. Our experimental results showed that the proposed ECA-HRNet achieved the average precision of 73.4% on the COCO2017 test-dev set and the average precision of 86.4% on the KDSJ test set using the ground truth bounding boxes. These research results can provide guidance for auxiliary training and motion evaluation of ski jumpers.
跳台滑雪是一项高速运动，这使得难以主观准确地分析技术动作。为了解决这个问题，我们提出了一种基于图像的姿势估计方法来分析跳台滑雪运动员的动作。首先，构建了一个跳台滑雪运动员的图像关键点数据集（KDSJ）。接下来，为了提高跳台滑雪运动员姿势估计的精度，我们在高分辨率网络（HRNet）的残差结构中嵌入了一个高效的通道注意力（ECA）模块，以融合更多有用的特征信息。在训练阶段，我们使用了迁移学习方法，即在 Common Objection in Context (COCO2017) 上进行预训练，以从 COCO2017 中获取特征知识，用于跳台滑雪运动员姿势估计任务。最后，使用跳台滑雪运动员的检测关键点来分析运动特征，以髋部和膝部角度随时间（帧数）的变化为例。我们的实验结果表明，所提出的 ECA-HRNet 在 COCO2017 测试集上的平均精度为 73.4%，在 KDSJ 测试集上的平均精度为 86.4%，使用的是地面真实边界框。这些研究结果可以为跳台滑雪运动员的辅助训练和运动评估提供指导。
