Curling is a strategic team sport that presents unique challenges for artificial intelligence (AI) research, particularly in decision-making and physical simulation. However, a significant barrier to AI development in curling is the lack of structured and accessible datasets. This project aims to address this gap by leveraging standardized video feeds from Curling Stadium to generate datasets suitable for AI research.Our approach involves developing software that uses image detection models YOLO (You Only Look Once) and SAM (Segment Anything Model) to analyze YouTube videos of curling matches, tracking objects such as rocks and players to gather data on their positions and movements.The expected outcome of the larger project is a structured and scalable dataset that can be used for AI-based curling research, including game strategy analysis and predictive modeling. This project lays the foundation for broader AI applications in curling by automating data collection, enabling machine learning models to analyze strategic decision-making, and fostering human-AI collaboration in sports analytics.
