Automatic initialization for 3D soccer player tracking

As a special application of computer vision, automatic sports video analysis has been studied by some researchers. This sports video analysis via computer vision is a moderately challenging problem: it is more difficult than analyzing a video of a few laboratory members acting as in a simple scenario and is easier than analyzing a video of crowded people at a subway station. So the success of an analysis heavily depends on how much one can exploit the prior information on the sport and setting. The most challenging and important part would be the tracking of players (and ball). With a multi-camera system, 3D tracking is feasible which is much more meaningful than 2D tracking for the analysis. As an initial step of 3D player tracking from multi-view soccer videos, this paper deals with automatic initialization of player positions. Initial 3D positions can be estimated by exploiting some conditions of a soccer match. To make it robust, prior knowledge on the features of players is learnt by support vector machines (SVM). Experimental results show that the proposed system is efficient for general soccer sequences.

► Initial locations and classes for players are automatically computed from multi-views.
► SVMs for size and color are used to get # of people inside and the class of a blob.