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Application of motion trajectory recognition based on remote sensing image optical processing in optimizing swimming training schemes

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Abstract

The optimization of swimming training scheme is of great significance to improve the training efficiency and competitive performance of swimmers. However, the traditional swimming training programs often lack of personalized and scientific, can not meet the characteristics and needs of different athletes. The aim of this paper is to realize the automatic recognition of motion trajectory through optical processing technology, so as to improve the swimming training scheme. The optical processing technology of remote sensing image is used to collect video images during swimming training, and the optical processing algorithm is used to extract the trajectory information of swimmers in the images. Then image processing and pattern recognition technology are used to identify and analyze the trajectory, so as to obtain accurate trajectory data. Finally, based on the analysis results, the swimming training scheme is optimized and personalized training suggestions are provided. Through the experiment and result analysis, the effectiveness of optical processing in the optimization of swimming training scheme is verified. Through the automatic identification and analysis of the movement trajectory, the swimmer’s skill and ability level can be more accurately understood, thus providing a scientific basis for the formulation and adjustment of training programs. The optical processing can also monitor and record the swimmers' trajectory changes in real time, providing real-time feedback and guidance for coaches to further improve the training effect.

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Authors and Affiliations

  1. Sports Department of Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Wei Jiang

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  1. Wei Jiang
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W.J. has contributed to the paper’s analysis, discussion, writing, and revision.

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Correspondence to Wei Jiang.

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Jiang, W. Application of motion trajectory recognition based on remote sensing image optical processing in optimizing swimming training schemes. Opt Quant Electron 56, 264 (2024). https://doi.org/10.1007/s11082-023-05874-7

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