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相关概念视频

Detection of Black Holes01:10

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
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When a rigid body is hanging freely from a fixed pivot point and is displaced, it oscillates similar to a simple pendulum and is known as a physical pendulum. The period and angular frequency of a physical pendulum are obtained by using the small-angle approximation and drawing parallels with a spring-mass system. The small-angle approximation (sinθ=θ) is valid up to about 14°.
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Design and Analysis for Fall Detection System Simplification
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用单一静态摄像头进行拳击击探测.

Piotr Stefański1, Jan Kozak1, Tomasz Jach1

  • 1Department of Machine Learning, University of Economics in Katowice, 1 Maja 50, 40-287 Katowice, Poland.

Entropy (Basel, Switzerland)
|August 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种计算机视觉系统,用于在奥林匹克拳击比赛中自动检测拳击. 使用卷积神经网络和图像操纵,它从单个摄像头录像中分析拳击手的表现,实现了高准确性.

关键词:
背景减去 减去 背景减去拳击手检测检测系统战斗运动分析分析冲击探测器检测的打击.

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科学领域:

  • 运动分析 运动分析
  • 计算机视觉 计算机视觉
  • 生物机械分析 生物机械分析

背景情况:

  • 体育运动中的球员表现监测越来越多地利用计算机视觉,因为它的非侵入性.
  • 传统的基于传感器的系统可以干扰运动员的设备,而基于摄像头的方法提供了更大的灵活性.
  • 计算机视觉系统为教练,记者和观众在拳击等体育运动中提供了宝贵的见解.

研究的目的:

  • 开发和评估一个系统,用于在奥林匹克拳击运动中使用单一静态摄像头自动检测拳击.
  • 探索图像处理技术,以提高计算机视觉模型的分类性能.
  • 创建一个功能系统,能够分析拳击场景,识别拳击手,检测拳击.

主要方法:

  • 使用欧几里德距离来测量拳击手之间的空间分离.
  • 采用卷积神经网络 (CNN) 来对盒装镜头进行分类.
  • 在训练之前实施和测试三个不同的图像处理策略,以优化分类器性能.

主要成果:

  • 拟议的系统在分类框架方面实现了95%的平衡精度.
  • 具体的性能指标包括包含穿孔的的49%F1得分和没有穿孔的的97%准确性.
  • 开发了一个工作系统,证明了能够用检测到的动作标记盒子和标签框架的能力.

结论:

  • 开发的计算机视觉系统有效地检测到高精度的奥林匹克拳击中的拳击.
  • 图像操纵技术可以显著提高基于CNN的分类器在体育分析中的性能.
  • 该系统提供了一个可行的工具,用于在拳击中进行自动性能分析,使各种利益相关者受益.