In Research (Washington, D.C.)
The real-time application of artificial intelligence (AI) technologies in sports is a long-standing challenge owing to large spatial sports field, complexity, and uncertainty of real-world environment, etc. Although some AI-based systems have been applied to sporting events such as tennis, basketball, and football, they are replayed after the game rather than applied in real time. Here, we present an AI-based curling game system, termed CurlingHunter, which can display actual trajectories, predicted trajectories, and house regions of curling during the games via a giant screen in curling stadiums and a live streaming media platform on the internet in real time, so as to assist the game, improve the interest of watching game, help athletes train, etc. We provide a complete description of CurlingHunter' architecture and a thorough evaluation of its performances and demonstrate that CurlingHunter possesses remarkable real-time performance (~9.005 ms), high accuracy (30 ± 3 cm under measurement distance > 20 m), and good stability. CurlingHunter is the first, to the best of our knowledge, real-time system that can assist athletes to compete during the games in the history of sports and has been successfully applied in Winter Olympics and Winter Paralympics. Our work highlights the potential of AI-based systems for real-time applications in sports.
Shi Xuanke, Wang Quan, Wang Chao, Wang Rui, Zheng Longshu, Qian Chen, Tang Wei
2022
