Investigación sobre el método de descomposición de acciones de imágenes de vídeo de fútbol basado en aprendizaje profundo

• Haoyu Wang ^[2] ; Lanfeng Wang ^[3] ; Litong Wang ^[1] ; Xinran Wang ^[4]
  1. [1] Gdansk University of Physical Education and Sport

     Gdansk University of Physical Education and Sport

     Gdańsk, Polonia

     
  2. [2] Guangzhou Institute of Applied Science and Technology
  3. [3] Guizhou City Vocational College
  4. [4] Shandong Culture and Tourism Investment Group Financial Leasing Co., Ltd,

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• Localización: Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte, ISSN-e 1577-0354, Vol. 25, Nº. 100, 2025, págs. 313-329
• Idioma: español
• Títulos paralelos:
  • Research on action decomposition method of football video image based on deep learning
• Enlaces
  • Texto completo
• Resumen
  • español

    Para mejorar el dominio de las habilidades futbolísticas, se propone un método de descomposición de acciones en imágenes de vídeo basado en aprendizaje profundo. Combinando este método con los principios del aprendizaje profundo, se recopilan las características de las acciones en las imágenes de vídeo, se optimiza el algoritmo de reducción de ruido y se simplifican los pasos de descomposición. Finalmente, el experimento demuestra que este método presenta una alta aplicabilidad práctica y cumple plenamente con los requisitos de la investigación.

  • English

    In order to better master football skills, a football video image action decomposition method based on deep learning is proposed. Combined with the principle of deep learning, the action characteristics of football video image are collected, the denoising algorithm of football video action image is optimized, and the action decomposition steps of football video image are simplified. Finally, the experiment proves that the football video image action decomposition method based on deep learning has high practicability in the process of practical application, fully meet the research requirements.

• Referencias bibliográficas
  • Abdellaoui, M., & Douik, A. (2020). Human Action Recognition in Video Sequences Using Deep Belief Networks. Traitement du Signal,37(1)....
  • Acevedo, J., Boden, A. L., Greif, D. N., Emerson, C. P., Ruiz, J. T., Jose, J., Feigenbaum, L. A., & Kaplan, L. D. (2021). Distal...
  • Ahmad, T., Jin, L., Lin, L., & Tang, G. (2021). Skeleton-based action recognition using sparse spatio-temporal GCN with edge effective...
  • Anvarov, F., Kim, D. H., & Song, B. C. (2020). Action recognition using deep 3D CNNs with sequential feature aggregation and attention....
  • Atto, A. M., Benoit, A., & Lambert, P. (2020). Timed-image based deep learning for action recognition in video sequences. Pattern...
  • Climent-Perez, P., Atemporal attention using alternative skeletal and video pre-processing. Sensors,21(3), 1005.
  • Feng, Y. (2020). Mobile terminal video image fuzzy feature extraction simulation based on SURF virtual reality technology. Ieee Access,8,...
  • Gao, L., Li, T., Song, J., Zhao, Z., & Shen, H. T. (2020). Play and rewind: Context-aware video temporal action proposals....
  • Hua, M., Gao, M., & Zhong, Z. (2021). Scn: dilated silhouette convolutional network for video action recognition. Computer aided geometric...
  • Jain, D. K., Zhang, Z., & Huang, K. (2020). Multi angle optimal pattern-based deep learning for automatic facial expression recognition....
  • Jenner, S., Belski, R., Devlin, B., Coutts, A., Kempton, T., & Forsyth, A. (2021). A qualitative investigation of factors influencing...
  • Jiang, H., & Tsai, S.-B. (2021). An empirical study on sports combination training action recognition based on SMO algorithm...
  • Kim, D. H., Anvarov, F., Lee, J. M., & Song, B. C. (2021). Metric-based attention feature learning for video action recognition....
  • Kong, Y., Wang, Y., & Li, A. (2021). Spatiotemporal saliency representation learning for video action recognition. IEEE Transactions...
  • Kumaravel, S., & Veni, S. (2019). A unified model of video-based human action categorization using Chaotic Quantum Swarm Intelligence...
  • Lei, Q., Zhang, H.-B., Du, J.-X., Hsiao, T.-C., & Chen, C.-C. (2020). Learning effective skeletal representations on RGB video for fine-grained...
  • Lejonagoitia-Garmendia, M., Gustran-Iglesias, I., Gil, S., Ortuondo, J., & Sarasola-Ruiz, L. (2023). Foot injuries in sport climbers:...
  • Li, H., Cryer, S., Acharya, L., & Raymond, J. (2020). Video and image classification using atomisation spray image patterns...
  • Li, Q., Yang, W., Chen, X., Yuan, T., & Wang, Y. (2020). Temporal segment connection network for action recognition. Ieee Access,8,...
  • McCord, A., Cocks, B., Barreiros, A. R., & Bizo, L. A. (2020). Short video game play improves executive function in the oldest old living...
  • Podoprosvetov, A., Alisejchik, A., & Orlov, I. (2021). Comparison of action recognition from video and IMUs. Procedia Computer...
  • Shamsolmoali, P., Celebi, M. E., & Wang, R. (2020). Advances in deep learning for real-time image and video reconstruction and processing....
  • Sharma, V., Gupta, M., Kumar, A., & Mishra, D. (2021). EduNet: a new video dataset for understanding human activity in the classroom environment....
  • Sun, C., Song, H., Wu, X., Jia, Y., & Luo, J. (2021). Exploiting informative video segments for temporal action localization. IEEE...
  • Wang, T., Chen, Y., Lin, Z., Zhu, A., Li, Y., Snoussi, H., & Wang, H. (2020). Recapnet: Action proposal generation mimicking human...
  • Zebhi, S., Al‐Modarresi, S. M. T., & Abootalebi, V. (2020). Converting video classification problem to image classification with global...