<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Because the current sports group competition middle school students’ motion video recognition is complicated, the article uses Lagrangian mathematical equations to classify the motion videos. Based on the spatial smoothness of the flicker parameter function, this paper proposes a Lagrangian mathematical method for detecting local motion regions. The research results show that the motion differential equation and numerical calculation method of the video derived in the article are correct.</p>
</abstract>

Because the current sports group competition middle school students’ motion video recognition is complicated, the article uses Lagrangian mathematical equations to classify the motion videos. Based on the spatial smoothness of the flicker parameter function, this paper proposes a Lagrangian mathematical method for detecting local motion regions. The research results show that the motion differential equation and numerical calculation method of the video derived in the article are correct.

Lagrange’s Mathematical Equations in the Sports Training of College Students

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Because the current sports group competition middle school students’ motion video recognition is complicated, the article uses Lagrangian mathematical...

Lagrange’s Mathematical Equations in the Sports Training of College Students

Pubblicato online: 11 giu 2023

Pagine: 363 - 372

Ricevuto: 19 gen 2022

Accettato: 14 mar 2022

DOI: https://doi.org/10.2478/amns.2022.2.0184

Parole chiave
Sports training, Video classification, Classification storage, Lagrange-Gaussian transform

© 2023 Zhongyu Wan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Figure 2

Figure 3

Lagrange’s Mathematical Equations in the Sports Training of College Students

Pubblicato online: 11 giu 2023

Pagine: 363 - 372

Ricevuto: 19 gen 2022

Accettato: 14 mar 2022

DOI: https://doi.org/10.2478/amns.2022.2.0184

Parole chiaveSports training, Video classification, Classification storage, Lagrange-Gaussian transform

© 2023 Zhongyu Wan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Figure 2

Figure 3

Parole chiave
Sports training, Video classification, Classification storage, Lagrange-Gaussian transform