Abstract
Based on optical image enhancement, this paper uses convolutional neural network to detect key points of swimming posture. In swimming posture analysis, key point detection can provide information about posture accuracy and evaluation. However, due to the change of illumination conditions, the reduction of image quality and occlusion, the accuracy and stability of key point detection are limited. In order to solve this problem, an optical image enhancement method based on convolutional neural network is proposed in this paper. First, a specific convolutional neural network architecture is used to extract features from input images. Then optical image enhancement technology is used to preprocess the input image to improve the image quality. Next, the trained convolutional neural network model is used to detect key points. Experimental results show that the proposed method has good performance under different illumination conditions and image quality. Therefore, this study provides a new idea and method for the application of optical image enhancement in the detection of key points in swimming posture analysis.
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Bin Xu has contributed to the paper’s analysis, discussion, writing, and revision.
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Xu, B. Optical image enhancement based on convolutional neural networks for key point detection in swimming posture analysis. Opt Quant Electron 56, 260 (2024). https://doi.org/10.1007/s11082-023-05875-6
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DOI: https://doi.org/10.1007/s11082-023-05875-6