Intelligent Recognition of Sports Athletes’ Wrong Movements Based on Computer Vision Technology
- DOI
- 10.2991/978-94-6463-230-9_164How to use a DOI?
- Keywords
- computer vision technology; A wrong action; Intelligent recognition
- Abstract
In the process of sports athletes training and competition, they will inevitably have some wrong actions, if only rely on artificial methods to judge the accuracy of these actions, then there will be a great deviation. In the development and application of computer vision technology, it can effectively improve the accuracy of behavior identification by applying it to the intelligent identification of behavior errors. Therefore, this paper first makes a simple introduction to the computer vision technology, it can be collected image for digital analysis, and has a strong practicability. On this basis, through the analysis of athletes’ motion characteristics, the use of Bayesian algorithm to realize the recognition of incorrect motion, so as to form a three-dimensional visual detection model. By testing the 3D-Visual Visual Method model, the correctness of the proposed method in incorrect behavior identification is verified, and the feasibility of the proposed method in incorrect behavior identification is verified.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Zhihong Yin PY - 2023 DA - 2023/09/04 TI - Intelligent Recognition of Sports Athletes’ Wrong Movements Based on Computer Vision Technology BT - Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023) PB - Atlantis Press SP - 1360 EP - 1366 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-230-9_164 DO - 10.2991/978-94-6463-230-9_164 ID - Yin2023 ER -