Sage Journals: Discover world-class research

Abstract

Athletics is a highly technical and comprehensive sport that requires not only the technical ability of athletes but also strong physical fitness. The traditional training method mainly relies on the experience and observation of coaches, lacking objective and quantitative analysis. On this basis, this article proposed an EfficientPose model based on mobile neural networks and combined it with the RANSAC matching algorithm to achieve real-time analysis and optimization of the process of track and field sports. The significance of this research lies in its potential to enhance athletic performance through advanced motion analysis. The purpose of the study was to develop a method for real-time analysis and optimization of athlete movements. Firstly, motion data was collected through a camera during the movement process. Secondly, the EfficientPose model was used to locate key moving points in real-time. Targeted training plans were then proposed to improve and optimize movements. The research results indicated that the EfficientPose model could effectively analyze and optimize athlete movements during training.

Keywords

MobileNet network model RANSAC matching algorithm EfficientPose model track and field athletes motion improvement

Get full access to this article

View all access options for this article.

References

Wang

Chen

. Effect analysis of functional physical fitness training in youth amateur track and field training based on improved genetic algorithm under functional analysis. Rev Psicol Deporte 2023; 32(4): 122–132.

Zhu

. Influence of nutritional supplementation and sports training on the physical fitness of track and field athletes. Mol Cell BioMech 2022; 19(2): 89–96.

Duan

. Study on the influence of training methods on track and field athletes’ performance. Rev Psicol Deporte 2023; 32(3): 264–276.

Zheng

Man

. An improved logistic regression method for assessing the performance of track and field sports. Comput Intell Neurosci 2022; 2022: 1495.

H-Y

M-Y

Cai

D-C

, et al. Research progress of grating projection on machine 3D topography inspection technology. Chinese Optics 2023; 16(3): 83.

Song

Peng

. A review of machine vision research and development. Equipment Manufacturing Technology 2019; 6: 213–216.

Zhang

Liu

, et al. Human behavior detection based on OpenPose model and ELM. Information and Computers 2020; 32(24): 40–43.

Wang

Jun

. A human skeleton keypoint detection method based on OpenPose slim model. Comput Appl 2019; 39(12): 3503.

Zheng

Guangyang

. A review of research on the application of deep learning in image recognition. Computer Engineering and Applications 2019; 55(12): 20–36.

10.

Wang

Zhu

Yang

Woo

. Detect2Interact: Localizing object key field in visual question answering with LLMs.

11.

Zhang

Fang

Wang

, et al. Efficientpose: Efficient human pose estimation with neural architecture search. Computational Visual Media, 2021, 7: 335-347. IEEE Intell Syst; 39(3): 35–44.

12.

Groos

Ramampiaro

Ihlen

EAF

. EfficientPose: Scalable single-person pose estimation. Appl Intell 2021; 51(4): 2518–2533.

13.

Wang

Yuan

. An experiment-based review of low-light image enhancement methods. IEEE Access 2020; 8: 87884–87917.

14.

Wang

Zhang

Hong-Hai

Shen

, et al. Overview of image enhancement algorithms. Chinese Optics 2017; 10(4): 438–448.

15.

Peng

Feng

Zhai

. Visual saliency detection based on global guidance map and background attention. IEEE Access 2023; 12: 95434–95446.

16.

Kattenborn

Leitloff

Schiefer

. Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS J Photogrammetry Remote Sens 2021; 173: 24–49.

17.

Alzubaidi

Zhang

Humaidi

, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021; 8: 1–74.

18.

Sherstinsky

. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys Nonlinear Phenom 2020; 404: 132306.

19.

Shewalkar

Nyavanandi

Ludwig

. Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. J Artif Intell Soft Comput Res 2019; 9(4): 235–245.

20.

Patil

Banyal

. A dynamic unconstrained feature matching algorithm for face recognition. J Adv Inf Technol 2020; 11(2): 103–108.

21.

Liu

. Improved RANSAC features image-matching method based on SURF. J Eng 2019; 2019(23): 9118–9122.

22.

. A population size dynamic control method based on Euclidean distance. Journal of Electronics and Information 2022; 44(6): 2195–2206.

23.

Smagulova

James

. A survey on LSTM memristive neural network architectures and applications. Eur Phys J Spec Top 2019; 228(10): 2313–2324.

24.

Gai

Chen

Yuan

. A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Comput Appl 2023; 35(19): 13895–13906.

Motion analysis and training optimization for track and field athletes based on machine vision

Abstract

Keywords

Get full access to this article

References