通过改进的YOLOv8-Pose以有效的局部注意力来对羽毛球运动员进行增强的姿势估计
Yijian Wu1, Zewen Chen1, Hongxing Zhang2
1College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究通过使用改进的YOLOv8-Pose模型和高效的局部注意力 (ELA) 机制,提高了羽毛球对人类姿势的估计. 新的框架在运动员的关键点定位方面实现了卓越的准确性,推进了体育分析.
科学领域:
- 计算机视觉 计算机视觉
- 运动分析 运动分析
- 人工智能的人工智能
背景情况:
- 准确的人体姿势估计对于体育分析至关重要,特别是在羽毛球,但由于有限的数据集和复杂的运动员运动,面临着挑战.
- 现有的姿势估计模型难以满足羽毛球的特定需求,阻碍了性能分析和训练的进步.
研究的目的:
- 专门为羽毛球运动员开发一个增强的姿势估计框架.
- 在羽毛球的背景下,提高关键点定位精度和整体姿势估计性能.
- 通过引入新的机制和专门的数据集来解决当前模型的局限性.
主要方法:
- 改进的YOLOv8-Pose架构被用作增强框架的基础.
- 引入了一种高效的局部注意力 (ELA) 机制,以捕捉细粒度的空间依赖性和上下文信息.
- 创建了一个新的羽毛球姿势数据集,使用微软Kinect v2摄像头创建了4000个注释样本,通过深度辅助注释和视觉检查进行了改进.
主要成果:
- 拟议的ELA增强的YOLOv8-Pose模型在关键评估指标中表现出卓越的准确性,包括平均平方误差 (MSE),对象关键点相似性 (OKS) 和正确关键点百分比 (PCK).
- ELA机制显著提高了关键点定位的准确性和位估计的整体有效性.
- 对比分析为体育姿势估计的注意力模块集成提供了可概括的见解.
结论:
- 开发的ELA增强的YOLOv8-Pose框架有效地解决了羽毛球人类姿势估计方面的挑战.
- 该研究强调了该方法在体育视觉任务中具有更广泛应用的潜力.
- 创建一个专门的羽毛球姿势数据集为未来该领域的研究提供了宝贵的资源.
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