在羽毛球中使用玩家信息预测飞轨迹的未来预测
Yuka Nokihara1, Ryo Hachiuma1, Ryosuke Hori1
1Graduate School of Science and Technology, Keio University, Yokohama 223-8852, Japan.
Journal of imaging
|May 26, 2023
概括
使用视频分析和玩家数据预测羽毛球飞轨迹显著提高了准确性. 这种方法在快节奏的比赛中提高了球员的表现和战略决策.
科学领域:
- 运动科学 运动科学 运动科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 视频分析对于像羽毛球这样的网球运动至关重要.
- 准确的轨迹预测有助于球员的表现和战略开发.
研究的目的:
- 为了分析数据,预测未来的羽毛球飞轨迹.
- 通过增强的预测模型,为玩家提供竞争优势.
主要方法:
- 从比赛视频中提取球员并分析他们的姿势.
- 开发一个时间序列模型,结合机和玩家数据.
- 将预测准确度与基线方法进行比较.
主要成果:
- 拟议的方法提高了精度13%,仅仅是空中飞机的位置.
- 与使用飞和玩家位置的方法相比,准确性增加了8.4%.
- 整合玩家姿势数据可以提高轨迹预测.
结论:
- 开发的方法在羽毛球轨迹预测方面取得了重大进展.
- 将玩家生物力学与飞动力学相结合,可以提高预测准确度.
- 这种方法可以应用于增强羽毛球运动员的训练和游戏策略.
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