An Unsupervised Approach for Segmentation and Clustering of Soccer Players

In this work we consider the problem of soccer team discrimination. The approach we propose starts from the monocular images acquired by a still camera. The first step is the soccer player detection, performed by means of background subtraction. An algorithm based on pixels energy content has been implemented in order to detect moving objects. The use of energy information, combined with a temporal sliding window procedure, allows to be substantially independent from motion hypothesis. Colour histograms in RGB space are extracted from each player, and provided to the unsupervised classification phase. This is composed by two distinct modules: firstly, a modified version of the BSAS clustering algorithm builds the clusters for each class of objects. Then, at runtime, each player is classified by evaluating its distance, in the features space, from the classes previously detected. Algorithms have been tested on different real soccer matches of the Italian Serie A.