Tennis Training Action Video Segmentation on Account of Deep Learning and Multi-Scale Optimization

Using the method of image analysis, the exploration of functional movements in tennis physical training is realized, and the exploration method of functional movements in tennis physical training based on a deep learning model is proposed. Image frame integration feature detection and video detection technology are used to collect the images of tennis physical training functional movements. Combining image depth learning and edge contour feature detection methods, a feature detection model of tennis physical training functional movements images is established. Combining depth learning and multi-scale wavelet feature transformation methods, multi-scale feature segmentation of tennis physical training functional movements video collected images is carried out. The multi-scale decomposition model of the fusion image of tennis physical training functional action deep learning is constructed. Through the multi-scale wavelet feature transformation method of adaptive learning combined with deep learning, the joint feature point location and fuzzy action feature detection of tennis physical training functional action video capture image are carried out. According to the difference of action feature point location, image information fusion and optimization detection are realized, and image edge detail feature restoration and spectral component recombination methods are adopted. This paper constructs a continuous frame detection model of tennis physical training functional action video capture images, and realizes the action detection and optimization recognition of tennis physical training functional action video capture images. The simulation results show that this method has a high accuracy in detecting the functional movements of tennis physical training, and a good performance in recognizing the reliability of wrong movements, which improves the ability of analyzing and detecting the functional movements of tennis physical training.

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Publication Details

Journal
International Journal of Image and Graphics
Published
2026-04-30
DOI
https://doi.org/10.1142/s021946782850012x
Primary Topic
Advanced Technologies in Various Fields
Type
article
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article

Tennis Training Action Video Segmentation on Account of Deep Learning and Multi-Scale Optimization

Lin Leng
International Journal of Image and Graphics
Advanced Technologies in Various Fields
article

Tennis Training Action Video Segmentation on Account of Deep Learning and Multi-Scale Optimization

Lin Leng
article en

Abstract

Using the method of image analysis, the exploration of functional movements in tennis physical training is realized, and the exploration method of functional movements in tennis physical training based on a deep learning model is proposed. Image frame integration feature detection and video detection technology are used to collect the images of tennis physical training functional movements. Combining image depth learning and edge contour feature detection methods, a feature detection model of tennis physical training functional movements images is established. Combining depth learning and multi-scale wavelet feature transformation methods, multi-scale feature segmentation of tennis physical training functional movements video collected images is carried out. The multi-scale decomposition model of the fusion image of tennis physical training functional action deep learning is constructed. Through the multi-scale wavelet feature transformation method of adaptive learning combined with deep learning, the joint feature point location and fuzzy action feature detection of tennis physical training functional action video capture image are carried out. According to the difference of action feature point location, image information fusion and optimization detection are realized, and image edge detail feature restoration and spectral component recombination methods are adopted. This paper constructs a continuous frame detection model of tennis physical training functional action video capture images, and realizes the action detection and optimization recognition of tennis physical training functional action video capture images. The simulation results show that this method has a high accuracy in detecting the functional movements of tennis physical training, and a good performance in recognizing the reliability of wrong movements, which improves the ability of analyzing and detecting the functional movements of tennis physical training.

International Journal of Image and Graphics
Henan University of Engineering (CN)
Openalex Percentile: Top 6%
Advanced Technologies in Various Fields
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