School of Education, Zhanjiang University of Science and Technology, Zhanjiang, Guangdong 524084, China.
Graduate School, Guangzhou Sport University, Guangzhou, Guangdong 510500, China.
Comput Intell Neurosci. 2022 Jul 31;2022:7835241. doi: 10.1155/2022/7835241. eCollection 2022.
In order to improve the effectiveness of tennis teaching and enhance students' understanding and mastery of tennis standard movements, based on the three-dimensional (3D) convolutional neural network architecture, the problem of action recognition is deeply studied. Firstly, through OpenPose, the recognition process of human poses in tennis sports videos is discussed. Athlete tracking algorithms are designed to target players. According to the target tracking data, combined with the movement characteristics of tennis, real-time semantic analysis is used to discriminate the movement types of human key point displacement in tennis. Secondly, through 2D pose estimation of tennis players, the analysis of tennis movement types is achieved. Finally, in the tennis player action recognition, a lightweight multiscale convolutional model is proposed for tennis player action recognition. Meanwhile, a key frame segment network (KFSN) for local information fusion based on keyframes is proposed. The network improves the efficiency of the whole action video learning. Through simulation experiments on the public dataset UCF101, the proposed 3DCNN-based KFSN achieves a recognition rate of 94.8%. The average time per iteration is only 1/3 of the C3D network, and the convergence speed of the model is significantly faster. The 3DCNN-based recognition method of information fusion action discussed can effectively improve the recognition effect of tennis actions and improve students' learning and understanding of actions in the teaching process.
为了提高网球教学的有效性，增强学生对网球标准动作的理解和掌握，基于三维（3D）卷积神经网络架构，深入研究了动作识别问题。首先，通过 OpenPose 讨论了网球运动视频中人体姿势的识别过程。设计了运动员跟踪算法来定位运动员。根据目标跟踪数据，并结合网球的运动特点，实时进行语义分析，以区分网球中人体关键点位移的运动类型。其次，通过对网球运动员的 2D 姿势估计，实现对网球运动类型的分析。最后，在网球运动员动作识别中，提出了一种用于网球运动员动作识别的轻量级多尺度卷积模型。同时，提出了一种基于关键帧的关键帧段网络（KFSN）用于局部信息融合。该网络提高了整个动作视频学习的效率。通过在公共数据集 UCF101 上的仿真实验，基于 3DCNN 的 KFSN 实现了 94.8%的识别率。每次迭代的平均时间仅为 C3D 网络的 1/3，模型的收敛速度明显更快。讨论的基于信息融合动作的 3DCNN 识别方法可以有效提高网球动作的识别效果，提高学生在教学过程中对动作的学习和理解。
