Abstract
Sports action monitoring needs to overcome the influence of multiple interference factors and match the monitored movements with standard movements. In order to improve the effect of sports action monitoring, based on the feature similarity algorithm, this paper constructs a sports monitoring model based on the feature similarity model, and proposes a new optimization-driven deep learning framework to solve a series of image enhancement tasks. Moreover, this paper introduces a data-driven deep network to provide a fast descent direction for accelerating the solution, and introduces an error control mechanism inferred by optimization theory to judge the correctness of the network output result and correct it to ensure that the desired target solution can be obtained in the end. In addition, by adopting the idea of optimization-driven deep learning, a task-specific optimization-driven deep learning framework is constructed for complex tasks. Finally, this paper designs experiments to verify the performance of the model. The comparison results show that the model constructed in this paper has certain practical effects.
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Hu, X., Zong, B. & Pang, B. Simulation of sports action monitoring based on feature similarity model. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03046-7
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DOI: https://doi.org/10.1007/s12652-021-03046-7