Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Biomechanical analysis of upper airway airflow characteristics in children.

Sleep & breathing = Schlaf & Atmung·2026
Same author

Data-efficient unsupervised deep learning deformable SPECT/CT registration framework for voxel-level radionuclide therapy dosimetry: validation using clinical <sup>131</sup>I DTC therapy data.

EJNMMI physics·2026
Same author

The association between living environmental factors and hearing loss in Chinese middle-aged and older adults: Results from a cross-sectional and longitudinal study hearing loss and living environment.

BMC public health·2026
Same author

Nanocrystal-tailored recombination for all-perovskite tandem solar modules.

Nature·2026
Same author

Outcome of cerebral embolic protection during transcatheter aortic valve replacement in high-risk patient for stroke.

JTCVS structural and endovascular·2026
Same author

Structure-specific involvement in advanced cervical lymph node extranodal extension predicts prognosis in nasopharyngeal carcinoma: A multi-center study.

Oral oncology·2026

相关实验视频

Updated: Jun 27, 2026

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis
10:02

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

Published on: December 24, 2014

基于图像处理的足迹识别和轨迹跟踪在田径运动中.

Jiaju Zhu1, Zhong Zhang2, Runnan Liu3

  • 1School of Physical Education, Northeast Normal University, Changchun, 130024, China.

Scientific reports
|March 29, 2025
PubMed
概括

本研究介绍了一种使用支持矢量机 (SVM) 的图像处理技术,以准确地跟踪和分析运动员的脚动作. 该方法提高了运动员的表现,并降低了田径运动员受伤的风险.

关键词:
脚动作识别功能 脚动作识别功能SIFT (尺度不变特征转换) 的特征提取特征.在SVM算法中,SVM算法是田径和田野运动的运动.

更多相关视频

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Published on: April 23, 2020

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

相关实验视频

Last Updated: Jun 27, 2026

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis
10:02

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

Published on: December 24, 2014

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Published on: April 23, 2020

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

科学领域:

  • 运动科学 运动科学 运动科学
  • 生物力学 生物力学
  • 计算机视觉 计算机视觉

背景情况:

  • 足球运动的精确度对于田径运动的表现和伤害预防至关重要.
  • 传统的足迹分析方法缺乏精度和用户接受度.
  • 开发客观和准确的脚动作分析工具对于运动员训练至关重要.

研究的目的:

  • 开发和验证一种图像处理方法,用于准确识别和跟踪运动员的脚动作.
  • 通过精确的脚动作分析,提高田径运动员的表现并降低受伤风险.
  • 评估支持矢量机 (SVM) 算法的在运动运动的分类和标准化中的有效性.

主要方法:

  • 从奥运会田径比赛中提取了13秒的视频.
  • 使用基于支持矢量机 (SVM) 算法的图像处理技术.
  • 追踪运动员的足迹轨迹,提取特征点,并对运动进行分类.
  • 基于提取的特征标准化运动员行为,并比较标准化前后的表现.

主要成果:

  • 与其他算法相比,SVM算法展示了优越的分类准确性和识别性能.
  • 标准化田径运动的图像处理导致所有测试运动员 (0.4-0.6) 的表现改善.
  • 基于SVM的图像处理方法被证明是有效和可接受的体育训练应用程序.

结论:

  • 基于SVM算法的图像处理方法提供了一种可靠和有效的方法来分析和改进运动员的脚动作.
  • 这种技术可以显著提高田径运动员的表现,并可能减少与训练相关的伤害.
  • 开发的方法有望在体育科学和生物力学分析中得到更广泛的应用和扩展.