Lai Kalin Guanlun, Huang Hsu-Chun, Lin Wei-Ting, Lin Shang-Yi, Lin Kawuu Weicheng
Department of Computer Science & Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Data Brief. 2024 Apr 18;54:110438. doi: 10.1016/j.dib.2024.110438. eCollection 2024 Jun.
Tennis is a popular sport, and the introduction of technology has allowed players to diversify their training. Tennis ball tracking is currently a focal point, serving not only to assist referees but also to enhance sports analysis. We introduce the Tennis Shot Side-View and Top-View Dataset, which serves as an invaluable resource for analyzing tennis movements and verifying landing positions after flight. This dataset combines side-view and top-view video clips, capturing various shot types and player movements from both outdoor and indoor fields. The dataset includes the actual ball positions of each clip for verification purposes. The Tennis Shot Side-View and Top-View Dataset represents a significant advancement in tennis research. Its multidimensional nature opens doors for in-depth player analysis, performance enhancement, and strategy development. We believe that this dataset will be a valuable asset to the tennis community, fostering innovation and excellence in the sport.
网球是一项广受欢迎的运动，技术的引入使球员能够使他们的训练多样化。网球轨迹追踪目前是一个焦点，不仅有助于裁判，还能加强运动分析。我们引入了网球击球侧视图和顶视图数据集，它是分析网球动作和验证飞行后落点位置的宝贵资源。该数据集结合了侧视图和顶视图视频片段，捕捉了来自室外和室内场地的各种击球类型和球员动作。为了便于验证，该数据集包含了每个片段中球的实际位置。网球击球侧视图和顶视图数据集代表了网球研究的重大进展。其多维度性质为深入的球员分析、性能提升和策略制定打开了大门。我们相信，这个数据集将成为网球界的宝贵资产，促进这项运动的创新和卓越发展。
