Abstract：To address the shortcomings in the field of athlete movement recognition, such as high cost of bone data annotation, unreasonable frame selection, and insufficient accuracy of recognition methods, this paper proposes an athlete movement recognition method that combines dynamic frame selection and temporal motion enhancement. Firstly, based on video RGB data, a mechanism combining global and local dynamic frame selection is proposed, and dynamic programming and adaptive beam pruning are used to optimize frame selection to ensure global consistency and local diversity. Then, a plug-and-play time and motion enhancement module is proposed, which can be arbitrarily embedded into 2D convolutional neural networks to learn video action features. Finally, the rationality and superiority of the proposed recognition method are verified on two datasets. On the HMDB51 dataset, the performance is improved by 3.16% compared to the suboptimal performance, and on the PV dataset, it is improved by 2.41% compared to the suboptimal performance. The experiments show that the combination of the global and local dynamic frame selection mechanism in the proposed method is reasonable and can balance global consistency and local diversity. At the same time, the plug-and-play time and motion enhancement module can effectively learn the spatiotemporal relationship of actions in the video and has certain superiority.
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