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在基于深度学习的运动管理场景中对象检测.

Baocheng Pei1, Yanan Sun2, Yebiao Fu3

  • 1School of Physical Education, Jinjiang College, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.

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概括

这项研究引入了一种用于运动管理的新监督物体检测方法,改进了运动员表现分析. 该方法增强了时间信息捕获和目标特征提取,以更准确地识别体育活动.

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科学领域:

  • 计算机视觉 计算机视觉
  • 运动科学 运动科学 运动科学
  • 机器学习 机器学习

背景情况:

  • 准确识别运动员,设备和场地边界对于体育表现分析至关重要.
  • 现有的物体检测方法在体育场景中扎着时间信息丢失,多重定位,目标重叠和任务合.
  • 这些局限性阻碍了当前用于运动管理检测的网络模型的有效性.

研究的目的:

  • 开发一种新的监督物体检测方法,专门用于体育运动中的运动管理场景.
  • 通过增强时间信息捕获和特征提取来解决现有方法的局限性.
  • 提高在动态体育环境中检测多个目标的准确性和效率.

主要方法:

  • 设计了一个时空模块 (TSM),集成时间偏移和空间卷积,以捕捉运动场景的动态.
  • 实施了可变形的注意力机制,以促进单个目标行动的特征提取.
  • 引入了脱结构,以分离回归和分类任务,以提高检测性能.

主要成果:

  • 拟议的方法在开源数据集上实现了92.298%的平均平均精度 (mAP),超过了其他七个常见的对象检测网络.
  • 废弃性研究证实,每一个拟议的模块都对总体检测准确度做出了重大贡献.
  • 实验结果证明了该方法在运动管理检测场景中的有效性和优势.

结论:

  • 新的监督物体检测方法显著提高了运动管理中目标检测的准确性.
  • TSM模块,可变形的注意力和解结构是推动性能提高的关键创新.
  • 这种方法为先进的体育表现分析和运动管理提供了有前途的解决方案.