In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. The classifier for a single decomposed action is constructed to transform the automatic description problem of free gymnastic movements into a multilabel classification problem. With the increase in the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper for spatial-temporal feature extraction of the video. The extracted features are binary classified several times to achieve the goal of multilabel classification. To form a comparison experiment, the results of the classification are randomly combined into a sentence and compared with the results of the automatic description method to verify the effectiveness of the method. The multiclass classifier constructed in this paper is used for human motion pose classification and recognition tests, and the experimental results show that the human motion pose recognition algorithm based on multifeature fusion can effectively improve the recognition accuracy and perform well in practical applications.

中文翻译：

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使用人工智能自动构建和提取运动时刻特征变量

在本文中，我们研究了运动时刻特征变量的自动构建和提取，并通过人工智能构建了特定变量的提取。本文选择在小样本情况下性能较好的支持向量机作为分类器，一对一构造多类分类器，实现人体运动姿势的分类识别。构造单个分解动作的分类器，将自由体操动作的自动描述问题转化为多标签分类问题。随着特征提取网络深度的增加，实验效果增强；然而，二维卷积神经网络在提取特征时会丢失时间信息，所以本文采用三维卷积网络对视频进行时空特征提取。对提取的特征进行多次二元分类，达到多标签分类的目的。形成对比实验，将分类结果随机组合成一个句子，并与自动描述方法的结果进行比较，以验证该方法的有效性。本文构建的多类分类器用于人体运动姿态分类识别测试，实验结果表明，基于多特征融合的人体运动姿态识别算法能够有效提高识别精度，在实际应用中表现良好。