Piecewise linear prediction model for action tracking in sports

Recent tracking methods in professional team sports reach very high accuracy by tracking the ball and players. However, it remains difficult for these methods to perform accurate real-time tracking in amateur acquisition conditions where the vertical position or orientation of the camera is not controlled and cameras use heterogeneous sensors. This article presents a method for tracking interesting content in an amateur sport game by analyzing player displacements. Defining optical flow of the foreground in the image as the player motions, we propose a piecewise linear supervised learning model for predicting the camera global motion needed to follow the action.