Visual Players Detection and Tracking in Soccer Matches

In this paper we present a people tracking algorithm which is able to detect and track soccer players in complex situations with varying light conditions, high frame rate, and real time processing. Object segmentation is performed by means of an algorithm based on background subtraction. In order to cope with presence of moving objects and light changes during the background modeling phase, an approach based on the evaluation of pixels energy content has been developed. Detected objects are then classified by means of an unsupervised clustering algorithm that allows the solution of blobs splitting and merging problems. For people tracking purpose we propose a stochastic approach based on the evaluation of the maximum a posteriori probability(MAP). First of all the algorithm evaluates geometrical information on the blob overlapping and then applies a color feature classification to track players and solve blob merging situations. Experimental tests have been carried out on long soccer image sequences in different weather and light conditions.