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Area of Science:

  • Sports Technology
  • Artificial Intelligence
  • Real-time Systems

Background:

  • Real-time AI application in sports is challenging due to complex environments.
  • Existing AI sports systems often analyze games post-event, not live.
  • Curling presents unique challenges for real-time AI due to its spatial dynamics.

Purpose of the Study:

  • To develop and evaluate CurlingHunter, an AI system for real-time curling game analysis.
  • To enhance spectator engagement and athlete training through live data visualization.
  • To demonstrate the feasibility of real-time AI in a complex sport environment.

Main Methods:

  • Developed an AI-based system, CurlingHunter, for real-time curling.
  • Integrated trajectory prediction and visualization for actual and predicted paths.
  • Deployed the system for live display on stadium screens and internet streaming.

Main Results:

  • CurlingHunter achieved remarkable real-time performance with processing times around 9.005 ms.
  • High accuracy was maintained, with predictions within 30 ± 3 cm over distances greater than 20 m.
  • The system demonstrated good stability and was successfully applied in major international competitions.

Conclusions:

  • CurlingHunter is the first real-time AI system to assist athletes during curling games.
  • The system significantly improves the viewing experience and aids athlete training.
  • This work validates the potential of AI for real-time applications across various sports.