Changing the Game with AI | Soccer Video Analysis by Adrian Manchado & Tanner Cellio
Adrien Explains YOLO and SAM Integration
Adrien Manado details how Rosco AI uses YOLO for object detection and SAM 2 for tracking players over time. YOLO identifies players in each frame, and SAM 2 then tracks these players, assigning unique IDs to maintain continuity. This combination addresses the limitations of YOLO's lack of memory, enabling the system to understand player movements and interactions throughout the game. The integration of these models is crucial for generating meaningful insights.
Tanner: Kalman Filters Give Rosco Intuition
Tanner Cellio describes the use of Kalman filters to improve ball tracking, stating that these filters provide Rosco AI with "intuition." By predicting the ball's location in frames where it's not detected, the system compensates for imperfect vision. This predictive capability relies on features like velocity and trajectory, allowing Rosco to make educated guesses about the ball's position. This ensures more complete and accurate data for analysis.
