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

<p>The current action recognition analysis method is easily affected by factors such as background, illumination, and target angle, which not only has low accuracy, but also relies on prior knowledge. Research on the identification and analysis of technical and tactical movements in football. According to the characteristics of football video, a multi-resolution three-dimensional convolutional neural network is constructed by combining the convolutional neural network and the three-dimensional neural network. The supervised training algorithm is used to update the network weights and thresholds, and the video images are input into the input layer. After the convolutional layer, sub-sampling layer and fully connected layer and other network layers to obtain action recognition results. The principal component analysis method is used to reduce the dimension to process the action data set, and the Fourier transform method is used to filter out the principal component noise. The experimental results show that the method can effectively identify the technical and tactical movements of athletes from complex football game videos, and analyze the applied technical and tactical strategies. The average value of accuracy, recall and precision of technical and tactical analysis is as high as 0.96, 0.97, and 0.95, and the recognition and analysis effect has significant advantages.</p>
</abstract>

The current action recognition analysis method is easily affected by factors such as background, illumination, and target angle, which not only has low accuracy, but also relies on prior knowledge. Research on the identification and analysis of technical and tactical movements in football. According to the characteristics of football video, a multi-resolution three-dimensional convolutional neural network is constructed by combining the convolutional neural network and the three-dimensional neural network. The supervised training algorithm is used to update the network weights and thresholds, and the video images are input into the input layer. After the convolutional layer, sub-sampling layer and fully connected layer and other network layers to obtain action recognition results. The principal component analysis method is used to reduce the dimension to process the action data set, and the Fourier transform method is used to filter out the principal component noise. The experimental results show that the method can effectively identify the technical and tactical movements of athletes from complex football game videos, and analyze the applied technical and tactical strategies. The average value of accuracy, recall and precision of technical and tactical analysis is as high as 0.96, 0.97, and 0.95, and the recognition and analysis effect has significant advantages.

Analysis and Research on Technical and Tactical Action Recognition in Football Based on 3D Neural Network

Applied Mathematics and Nonlinear Sciences

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

The current action recognition analysis method is easily affected by factors such as background, illumination, and target angle, which not only has low...

Analysis and Research on Technical and Tactical Action Recognition in Football Based on 3D Neural Network

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: 1447 - 1462

Eingereicht: 12. Apr. 2022

Akzeptiert: 15. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00046

Schlüsselwörter
Three-dimensional neural network, Convolutional neural network, Three-dimensional convolutional neural network, Football, Action recognition, Technical and tactical analysis

© 2023 Chao Feng et al., published by Sciendo

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

Analysis and Research on Technical and Tactical Action Recognition in Football Based on 3D Neural Network

Online veröffentlicht: 28. Apr. 2023

Seitenbereich: 1447 - 1462

Eingereicht: 12. Apr. 2022

Akzeptiert: 15. Juli 2022

DOI: https://doi.org/10.2478/amns.2023.1.00046

SchlüsselwörterThree-dimensional neural network, Convolutional neural network, Three-dimensional convolutional neural network, Football, Action recognition, Technical and tactical analysis

© 2023 Chao Feng et al., published by Sciendo

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

Schlüsselwörter
Three-dimensional neural network, Convolutional neural network, Three-dimensional convolutional neural network, Football, Action recognition, Technical and tactical analysis