In the context of modern people increasingly paying attention to health and promoting aerobics, the amount of data and audiences of aerobics videos has grown rapidly, and its potential application value has attracted widespread attention from scientific research and industry perspectives. This article has integrated computer vision and deep learning related knowledge to realize the intelligent recognition and representation of specific human movements in aerobics video sequences. The study proposes an automatic recognition method for floor exercise videos based on three-dimensional convolutional networks and multilabel classification. Since two-dimensional convolutional neural networks (CNNs) lose time information when extracting features, so to overcome this, the proposed research uses three-dimensional convolutional networks to perform video recognition. The feature is taken in time and space, and the extracted features are subjected to multiple binary classifications to achieve the goal of multilabel classification. Various comparison and simulation experiments are conducted for the proposed research, and the experimental results prove the effectiveness and superiority of the approach.

中文翻译：

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基于三维卷积神经网络和多标签分类的健美操动作识别算法

在现代人越来越关注健康、提倡健美操的背景下，健美操视频的数据量和受众快速增长，其潜在的应用价值受到了科研和行业的广泛关注。本文融合了计算机视觉和深度学习相关知识，实现了健美操视频序列中特定人体动作的智能识别和表示。该研究提出了一种基于三维卷积网络和多标签分类的自由体操视频自动识别方法。由于二维卷积神经网络 (CNN) 在提取特征时会丢失时间信息，因此为了克服这一点，拟议的研究使用三维卷积网络来执行视频识别。在时间和空间上取特征，对提取的特征进行多次二元分类，达到多标签分类的目的。针对所提出的研究进行了各种比较和模拟实验，实验结果证明了该方法的有效性和优越性。