In this work, the novel task of detecting and classifying table tennis strokes solely using the ball trajectory has been explored. A single camera setup positioned in the umpire's view has been employed to procure a dataset consisting of six stroke classes executed by four professional table tennis players. Ball tracking using YOLOv4, a traditional object detection model, and TrackNetv2, a temporal heatmap based model, have been implemented on our dataset and their performances have been benchmarked. A mathematical approach developed to extract temporal boundaries of strokes using the ball trajectory data yielded a total of 2023 valid strokes in our dataset, while also detecting services and missed strokes successfully. The temporal convolutional network developed performed stroke recognition on completely unseen data with an accuracy of 87.155%. Several machine learning and deep learning based model architectures have been trained for stroke recognition using ball trajectory input and benchmarked based on their performances. While stroke recognition in the field of table tennis has been extensively explored based on human action recognition using video data focused on the player's actions, the use of ball trajectory data for the same is an unexplored characteristic of the sport. Hence, the motivation behind the work is to demonstrate that meaningful inferences such as stroke detection and recognition can be drawn using minimal input information.

翻译：本研究探索了仅利用球轨迹数据进行乒乓球击球动作检测与分类的新任务。采用位于裁判视角的单摄像头设置，采集了由四名职业乒乓球运动员执行的六类击球动作数据集。分别采用YOLOv4（传统目标检测模型）和TrackNetv2（基于时序热图的模型）对数据集中的球进行追踪，并对其性能进行了基准测试。通过基于球轨迹数据提取击球动作时间边界的数学方法，共获得数据集中2023个有效击球样本，同时成功检测出发球与漏击动作。所开发的时序卷积网络在完全未见数据上的击球识别准确率达到87.155%。基于球轨迹输入，训练了多种机器学习与深度学习模型架构用于击球识别，并依据其性能进行了基准比较。尽管乒乓球领域的击球识别已基于聚焦运动员动作的视频数据，通过人体动作识别方法得到广泛研究，但利用球轨迹数据进行同类分析仍属该运动尚未探索的特征。因此，本研究的动机在于证明：即使仅使用最小输入信息，也能提取诸如击球检测与识别等有意义的推断结果。