适应YOLOv7和YOLOv7_tiny用于足球多重检测与DeepSORT通过半监督系统进行跟踪
Jorge Armando Vicente-Martínez1, Moisés Márquez-Olivera1, Abraham García-Aliaga2
1Centro de Investigación e Innovación Tecnológica (CIITEC), Instituto Politécnico Nacional (IPN), Cerrada Cecati s/n Col. Sta. Catarina, Azcapotzalco, Mexico City 02250, Mexico.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究提高了使用YOLOv7和焦点损失的足球检测和跟踪,达到95%的准确性. DeepSORT算法进一步完善了轨迹分析,以改善体育分析.
科学领域:
- 计算机视觉 计算机视觉
- 运动分析 运动分析
- 机器学习 机器学习
背景情况:
- 对象识别和跟踪在体育分析中至关重要,特别是用于轨迹分析.
- 现有的方法在各种条件下准确检测和跟踪足球方面面临挑战.
研究的目的:
- 通过半监督网络推进足球检测和跟踪.
- 为了提高体育应用对象识别的准确性和稳定性.
主要方法:
- 实现了YOLOv7卷积神经网络架构.
- 集成焦点损失功能,以提高检测准确度.
- 使用DeepSORT算法进行精确的轨迹跟踪.
主要成果:
- 在足球球探测方面取得了95%的准确性.
- 与具有默认损失函数的传统方法相比,表现出优越的性能.
- 成功启用精确的轨迹跟踪,用于增强体育分析.
结论:
- 拟议的带有YOLOv7和焦点损失的半监控网络显著提高了足球检测和跟踪精度.
- 这种方法为体育分析提供了一个强大的解决方案,优于以前的方法.
- 集成DeepSORT提供了精确的轨迹分析,突出显示了模型的有效性.
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