Year: 2025
Type: article
Abstract: ABSTRACT Swimming sports are treated as modern competitive sports, and athletes need to standardize and correct their posture. Therefore, the recognition of swimming postures is considered as an important section the coaches implement training plans. Usually, the recognition of swimming postures is achieved through coach observation; however, this approach is inefficient and lacks sufficient accuracy. To address this issue, a novel recognition method is proposed. In the proposed method, different swimming postures are assigned a different score via using a two‐stage scoring mechanism. The feature regions of swimming postures can be accurately identified. Following that, the assigned score is put into the Softmax layer of the proposed convolutional neural networks. Finally, 4000 images including six swimming postures are used as an experimental set. The experimental results show that the proposed method achieves 92.73% testing accuracy and 89.03% validation accuracy in the recognition of the six swimming postures, defeating against the opponents. Meanwhile, our method outperforms some competitors in terms of training efficiency. The proposed two‐stage scoring mechanism can be used for image recognition in large‐scale scenarios. Moreover, the two‐stage scoring mechanism is independently of specific scenarios in process of assigning a score value for feature regions of images. Not only that, the two‐stage scoring mechanism can replace complex network structures, so as to reduce the work of training parameters.
Source: IET Image Processing
DOI: 10.1049/ipr2.70140
Authors: Xie Lina, Xianfeng Huang, Luo Jie, Jian Zheng
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,740
OpenAlex ID: W4411633731