A learning-based tracking for diving motions

A learning-based tracking algorithm for diving motions is presented in this paper. In this algorithm, a complex diving motion is considered as the combination of several simple sub-motions. The contour of the athlete in each sub-motion is represented by B-spline snake, which can be fitted to the real body contour by a recursive curve-fitting algorithm. By learning from the videos in a training set, the initial contour templates for each sub-motion are set up and each possible frame where a new sub-motion begins is found out, which allows the possibility of whole motion tracking. Experiments demonstrate that the proposed algorithm is robust and efficient in diving motions tracking.