基于PoseC3D的网球动作识别及评价方法

为了准确地识别及评价网球动作,将计算机视觉与网球运动相关知识相结合,提出了一种基 于PoseC3D的网球动作识别及评价方法。首先,使用基于 ResNet-50姿态估计模型对网球运动视频进行 人体目标检测并提取骨骼关键点;然后,使用在专业网球场采集的视频数据集进行 PoseC3D 模型训练,使 模型能够对网球的子动作进行分类;之后,使用动态时间规整算法对分类的动作进行评价;最后,基于采集 的视频数据集进行了大量实验。结果表明,提出的基于 PoseC3D的网球动作识别方法对6类网球子动作 的分类 Top1准确率可以达到90.8%。相较于基于图卷积网络的方法,比如 AGCN 和 ST-GCN,具有更 强的泛化能力;提出的基于动态时间规整的评分算法能够在动作分类后实时、准确地给出相应动作的评价 分数,从而减少了网球教师的工作强度,有效地提升了网球教学质量。

To accurately recognize and evaluate tennis actions, by combining computer vision with
tennis related knowledge, this paper proposes a tennis action recognition and evaluation method based on PoseC3D. Firstly, a pose estimation model based on resnet-50 is used to detect human targets in tennis video and extract bone key points. Secondly, the PoseC3D model is trained through the video data set collected in the professional tennis court, so that it can classify the sub actions of tennis. Thirdly, the dynamic time warping algorithm is used to evaluate the classified actions. Finally, based on the collected video data set, a large number of experiments are carried out. The results show that the Top1 accuracy of the proposed tennis action recognition method based on PoseC3D can reach 90. 8%. Compared with the methods based on graph convolution network, such as AGCN and ST-GCN, it has stronger generalization ability. Moreover, the proposed scoring algorithm based on dynamic time warping can give real-time and accurate evaluation scores for corresponding actions after action classification, reducing the work intensity of tennis teachers and effectively improving the quality of tennis teaching.