With the rapid technological advances in sports, the number of athletics increases gradually. For sports professionals, it is obligatory to oversee and explore the athletics pose in athletes’ training. Key frame extraction of training videos plays a significant role to ease the analysis of sport training videos. This paper develops a sports actions’ classification system for accurately classifying athlete’s actions. The key video frames are extracted from the sports training video to highlight the distinct actions in sports training. Subsequently, a fully convolutional network (FCN) is used to extract the region of interest (ROI) pose detection of frames followed by the application of a convolution neural network (CNN) to estimate the pose probability of each frame. Moreover, a distinct key frame extraction approach is established to extract the key frames considering neighboring frames’ probability differences. The experimental results determine that the proposed method showed better performance and can recognize the athlete’s posture with an average classification rate of 98%. The experimental results and analysis validate that the proposed key frame extraction method outperforms its counterparts in key pose probability estimation and key pose extraction.

中文翻译：

---

基于改进深度学习的运动训练关键帧提取

随着体育技术的飞速发展，田径项目的数量逐渐增加。对于体育专业人士来说，有义务监督和探索运动员训练中的田径姿势。训练视频的关键帧提取对于简化运动训练视频的分析起着重要作用。本文开发了一种运动动作分类系统，用于对运动员的动作进行准确分类。从运动训练视频中提取关键视频帧以突出运动训练中的不同动作。随后，使用全卷积网络 (FCN) 提取帧的感兴趣区域 (ROI) 位姿检测，然后应用卷积神经网络 (CNN) 来估计每帧的位姿概率。而且，考虑相邻帧的概率差异，建立了独特的关键帧提取方法来提取关键帧。实验结果表明，该方法表现出更好的性能，能够以98%的平均分类率识别运动员的姿势。实验结果和分析验证了所提出的关键帧提取方法在关键姿态概率估计和关键姿态提取方面优于同类方法。