基于改进时空图卷积网络的乒乓球击球动作识别
Recognition of Table-Tennis Action Based on Improved Spatio-Temporal Graph Convolutional Network
DOI: 10.12677/AIRR.2021.103025, PDF,    科研立项经费支持
作者: 刘明方, 尹真杰, 段晓东：大连民族大学大数据应用技术国家民委重点实验室，辽宁 大连；大连民族大学计算机科学与工程学院，辽宁 大连；汪语哲, 张皓天：大连民族大学大数据应用技术国家民委重点实验室，辽宁 大连；大连民族大学机电工程学院，辽宁 大连
关键词: 乒乓球击球；动作识别；骨骼关键点；图卷积网络；Striking of Table Tennis； Human Action Recognition； Skeleton Key Points； Graph Convolutional Net-work
摘要: 本文研究了计算机视觉辅助开展乒乓球训练中的乒乓球击球动作识别问题。基于骨骼关键点方式的动作识别算法，只对人体骨骼点的时空信息进行学习，可以去除环境、光线等干扰因素。通过摄像机采集了正手击球、反手击球、正手拉球、反手拉球和非击球动作5类动作在内的体育运动视频，使用OpenPose提取18个人体骨骼关键点，构建了乒乓球击球骨骼点数据集。根据乒乓球击球核心力量区域对ST-GCN网络的卷积核进行调整，最终训练模型的击球动作精准度可以达到98%；并在文章创建数据集之外的乒乓球击球动作视频上进行了泛化测试，对比ST-GCN网络的泛化效果，结果文章调整后的时空图卷积网络方法效果更好，具有较高的实用价值。
Abstract: The problem of table tennis training with assistance of computer video was studied in this paper. Action recognition algorithm based on method of key point of the bone only learns the spa-tio-temporal information of the human bone points, and can remove interference factors such as environment and light. Video of sports activities including forehand, backhand, forehand, backhand, and non-hit action was collected through the camera, and 18 key points of human bones were ex-tracted using OpenPose to construct a dataset of bones of players playing table tennis. Convolution kernel of the ST-GCN network was adjusted according to the core strength area of table tennis striking, and accuracy of the final training model’s striking action can reach 98%. Generalization test was performed on video of table tennis striking beyond data set proposed in this paper, and generalization effect showed that the proposed spatio-temporal graph convolutional network method showed better results and thus had higher practical value than the proposed ST-GCN net-work.
文章引用：刘明方, 汪语哲, 尹真杰, 张皓天, 段晓东. 基于改进时空图卷积网络的乒乓球击球动作识别[J]. 人工智能与机器人研究, 2021, 10(3): 248-256. https://doi.org/10.12677/AIRR.2021.103025

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