Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Martial Arts Moves Recognition Method Based on Visual Image

  • Husheng, Zhou (School of Physical Education, Huaibei Normal University)
  • Received : 2022.06.16
  • Accepted : 2022.09.14
  • Published : 2022.12.31
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Abstract

Intelligent monitoring, life entertainment, medical rehabilitation, and other fields are only a few examples where visual image technology is becoming increasingly sophisticated and playing a significant role. Recognizing Wushu, or martial arts, movements through the use of visual image technology helps promote and develop Wushu. In order to segment and extract the signals of Wushu movements, this study analyzes the denoising of the original data using the wavelet transform and provides a sliding window data segmentation technique. Wushu movement The Wushu movement recognition model is built based on the hidden Markov model (HMM). The HMM model is trained and taught with the help of the Baum-Welch algorithm, which is then enhanced using the frequency weighted training approach and the mean training method. To identify the dynamic Wushu movement, the Viterbi algorithm is used to determine the probability of the optimal state sequence for each Wushu movement model. In light of the foregoing, an HMM-based martial arts movements recognition model is developed. The recognition accuracy of the HMM model increases to 99.60% when the number of samples is 4,000, which is greater than the accuracy of the SVM (by 0.94%), the CNN (by 1.12%), and the BP (by 1.14%). From what has been discussed, it appears that the suggested system for detecting martial arts acts is trustworthy and effective, and that it may contribute to the growth of martial arts.

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References

  1. J. Liang, F. Xu, and S. Yu, "A multi-scale semantic attention representation for multi-label image recognition with graph networks," Neurocomputing, vol. 491, pp. 14-23, 2022. https://doi.org/10.1016/j.neucom.2022.03.057
  2. Z. Wu, H. Li, X. Wang, Z. Wu, L. Zou, L. Xu, and M. Tan, "New benchmark for household garbage image recognition," Tsinghua Science and Technology, vol. 27, no. 5, pp. 793-803, 2022. https://doi.org/10.26599/TST.2021.9010072
  3. W. Tang and H. Chen, "Research on intelligent substation monitoring by image recognition method," International Journal of Emerging Electric Power Systems, vol. 22, no. 1, pp. 1-7, 2021. https://doi.org/10.1515/ijeeps-2020-0189
  4. A. V. Savchenko, "Sequential three-way decisions in multi-category image recognition with deep features based on distance factor," Information Sciences, vol. 489, pp. 18-36, 2019. https://doi.org/10.1016/j.ins.2019.03.030
  5. S. Umer, B. C. Dhara, and B. Chanda, "NIR and VW iris image recognition using ensemble of patch statistics features," The Visual Computer, vol. 35, no. 9, pp. 1327-1344, 2019. https://doi.org/10.1007/s00371-018-1544-4
  6. W. J. Hu, T. Y. Xie, B. S. Li, Y. X. Du, and N. N. Xiong, "An edge intelligence-based generative data augmentation system for IoT image recognition tasks," Journal of Internet Technology, vol. 22, no. 4, pp. 765-778,
  7. H. Ling, W. Zhang, Y. Tao, and M. Zhou, "Research on network layer recursive reduction model compression for image recognition," Scientific Programming, vol. 2021, article no. 4054435, 2021. https://doi.org/10.1155/2021/4054435
  8. L. Jin, H. Liang, and C. Yang, "Sonar image recognition of underwater target based on convolutional neural network," Journal of Northwestern Polytechnical University, vol. 39, no. 2, pp. 285-291,
  9. G. Huang, L. Qiao, S. Khanna, P. A. Pavlovich, and S. Tiwari, "Research on fan vibration fault diagnosis based on image recognition," Journal of Vibroengineering, vol. 23, no. 6, pp. 1366-1382,
  10. A. Jafar and M. Lee, "High-speed hyperparameter optimization for deep ResNet models in image recognition," Cluster Computing, 2021. https://doi.org/10.1007/s10586-021-03284-6
  11. X. Yang, Y. Zhang, W. Lv, and D. Wang, "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, vol. 163, pp. 386-397, https://doi.org/10.1016/j.renene.2020.08.125
  12. S. Tang and M. Shabaz, "A new face image recognition algorithm based on cerebellum-basal ganglia mechanism," Journal of Healthcare Engineering, vol. 2021, article no. 3688881, 2021. https://doi.org/10.1155/2021/3688881
  13. Z. Tan and X. Liu, "ConvNet combined with minimum weighted random search algorithm for improving the domain shift problem of image recognition model," Applied Intelligence, vol. 52, no. 6, pp. 6889-6904, 2022. https://doi.org/10.1007/s10489-021-02767-8
  14. N. A. Andriyanov, V. E. Dementiev, and Y. D. Kargashin, "Analysis of the impact of visual attacks on the characteristics of neural networks in image recognition," Procedia Computer Science, vol. 186, pp. 495-502,
  15. F. Meng, B. Xu, T. Zhang, B. Muthu, and C. B. Sivaparthipan, "Application of AI in image recognition technology for power line inspection," Energy Systems, 2021. https://doi.org/10.1007/s12667-020-00414-8
  16. A. Jeya Christy and K. Dhanalakshmi, "Content-based image recognition and tagging by deep learning methods," Wireless Personal Communications, vol. 123, pp. 813-838, 2022. https://doi.org/10.1007/s11277-021-09159-8
  17. F. Wang, R. Hu, and Y. Jin, "Research on gesture image recognition method based on transfer learning," Procedia Computer Science, vol. 187, pp. 140-145, 2021.  https://doi.org/10.1016/j.procs.2021.04.044