In recent years, athlete action recognition has become an important research field for showing and recognition of athlete actions. Generally speaking, movement recognition of athletes can be performed through a variety of modes, such as motion sensors, machine vision, and big data analysis. Among them, machine vision and big data analysis usually contain significant information which can be used for various purposes. Machine vision can be expressed as the recognition of the time sequence of a series of athlete actions captured through camera, so that it can intervene in the training of athletes by visual methods and approaches. Big data contains a large number of athletes’ historical training and competition data which need exploration. In-depth analysis and feature mining of big data will help coach teams to develop training plans and devise new suggestions. On the basis of the above observations, this paper proposes a novel spatiotemporal attention map convolutional network to identify athletes’ actions, and through the auxiliary analysis of big data, gives reasonable action intervention suggestions, and provides coaches and decision-making teams to formulate scientific training programs. Results of the study show the effectiveness of the proposed research.

中文翻译：

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机器视觉和大数据驱动的运动员动作训练干预模型

近年来，运动员动作识别已经成为显示和识别运动员动作的重要研究领域。一般来说，运动员的动作识别可以通过多种模式执行，例如运动传感器，机器视觉和大数据分析。其中，机器视觉和大数据分析通常包含可用于各种目的的重要信息。机器视觉可以表示为对通过相机捕获的一系列运动员动作的时间序列的识别，因此它可以通过视觉方法和方法干预运动员的训练。大数据包含大量需要探索的运动员的历史训练和比赛数据。大数据的深入分析和特征挖掘将帮助教练团队制定培训计划并提出新建议。在上述观察的基础上，提出了一种新颖的时空注意图卷积网络来识别运动员的动作，并通过对大数据的辅助分析，给出合理的动作干预建议，并为教练和决策团队制定科学的方法。培训计划。研究结果表明了所提出研究的有效性。并提供教练和决策团队制定科学的培训计划。研究结果表明了所提出研究的有效性。并提供教练和决策团队制定科学的培训计划。研究结果表明了所提出研究的有效性。