Abstract
With the continuous development of competitive sports, higher requirements have been put forward for the athletic level and technical movements of track and field athletes. Running gait is a key factor that affects the competitive level and technical action of athletes. Therefore, a research on the recognition method of running gait of track and field athletes based on convolutional neural network is proposed. According to the internal noise type of running gait image, the least mean square filtering algorithm is selected as the preprocessing method of running gait image, and it is used to remove the noise of running gait image. Based on this, the LB P feature, Hu moment invariant feature and Haar like feature are extracted as the running gait characteristics, and the convolutional neural network model is designed. With the above designed convolutional neural network model as a tool, the running gait recognition program of track and field athletes is formulated, And determine the calculation formula of the relevant parameters, in order to obtain accurate results of track and field athletes’ running gait recognition. The experimental data show that the maximum accuracy of running gait recognition of track and field athletes obtained by using the proposed method is 94%, which fully proves that the proposed method has better performance in running gait recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qiu, Y., Gao, Z.: Recognition of abnormal gait active image sequences based on low rank decomposition. Comput. Simul. 38(6), 415–418 (2021)
Liu, S., Liu, D., Muhammad, K., Ding, W.: Effective template update mechanism in visual tracking with background clutter. Neurocomputing 458, 615–625 (2021)
Ferguson, E.L.: Multitask convolutional neural network for acoustic localization of a transiting broadband source using a hydrophone array. J. Acoust. Soc. America 150(1), 248–256 (2021)
Dong, S., Jin, Y., Bak, S.J., et al.: Explainable convolutional neural network to investigate the age-related changes in multi-order functional connectivity. Electronics 10(23), 3020 (2021)
Liu, S., Zhu, C.: Jamming recognition based on feature fusion and convolutional neural network. J. Beijing Inst. Technol. 31(2), 169–177 (2022)
Liu, S., Wang, S., Liu, X., et al.: Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE Trans. Fuzzy Syst. 29(1), 90–102 (2021)
Halász, M., Gerak, J., Bakonyi, P., et al.: Study on the compression effect of clothing on the physiological response of the athlete. Materials 15(1), 169–169 (2022)
Zhu, Z., Yao, C.: Application of attitude tracking algorithm for face recognition based on OpenCV in the intelligent door lock. Comput. Commun. 154(5), 390–397 (2020)
Lai, X., Rau, P.: Has facial recognition technology been misused? A user perception model of facial recognition scenarios. Comput. Hum. Behav. 124(8), 106894 (2021)
Liu, S., et al.: Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans. Multimedia 23, 2188–2198 (2021)
Luo, W., Ning, B.: High-dynamic dance motion recognition method based on video visual analysis. Sci. Program. 2022, 1–9 (2022)
Tha, B., Sk, B., Mt, B., et al.: Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers. Neurocomputing 489, 599–612 (2022)
Sattar, N.Y., Kausar, Z., Usama, S.A., et al.: FNIRS-based upper limb motion intention recognition using an artificial neural network for transhumeral amputees. Sensors 22(3), 726–733 (2022)
Zhang, K., Zhao, D., Liu, W.: Online vehicle trajectory compression algorithm based on motion pattern recognition. IET Intel. Transp. Syst. 16(8), 998–1010 (2022)
Muhammad, U., Yu, Z., Komulainen, J.: Self-supervised 2D face presentation attack detection via temporal sequence sampling. Pattern Recogn. Lett. 156(4), 15–22 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Lin, Q., Wang, J. (2024). Recognition of Running Gait of Track and Field Athletes Based on Convolutional Neural Network. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-031-50574-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-50574-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50573-7
Online ISBN: 978-3-031-50574-4
eBook Packages: Computer ScienceComputer Science (R0)