Football Players classification in a Multi-camera environment

In order to perform automatic analysis of sport videos ac- quired from a multi-sensing environment, it is fundamental to face the problem of automatic football team discrimination. A correct assignment of each player to the relative team is a preliminary task that together with player detection and tracking algorithms can strongly a®ect any high level semantic analysis. Supervised approaches for object classi¯- cation, require the construction of ad hoc models before the processing and also a manual selection of di®erent player patches belonging to the team classes. The idea of this paper is to collect the players patches com- ing from six di®erent cameras, and after a pre-processing step based on CBTF (Cumulative Brightness Transfer Function) studying and compar- ing di®erent unsupervised method for classi¯cation. The pre-processing step based on CBTF has been implemented in order to mitigate di®er- ence in appearance between images acquired by di®erent cameras. We tested three di®erent unsupervised classi¯cation algorithms (MBSAS - a sequential clustering algorithm; BCLS - a competitive one; and k-means - a hard-clustering algorithm) on the transformed patches. Results ob- tained by comparing di®erent set of features with di®erent classi¯ers are proposed. Experimental results have been carried out on di®erent real matches of the Italian Serie A. 1