Abstract

In order to perform automatic analysis of sport videos ac-quired from a multi-sensing environment, it is fundamental to face theproblem of automatic football team discrimination. A correct assignmentof each player to the relative team is a preliminary task that togetherwith player detection and tracking algorithms can strongly a®ect anyhigh level semantic analysis. Supervised approaches for object classi¯-cation, require the construction of ad hoc models before the processingand also a manual selection of di®erent player patches belonging to theteam 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 onCBTF (Cumulative Brightness Transfer Function) studying and compar-ing di®erent unsupervised method for classi¯cation. The pre-processingstep based on CBTF has been implemented in order to mitigate di®er-ence in appearance between images acquired by di®erent cameras. Wetested three di®erent unsupervised classi¯cation algorithms (MBSAS - asequential 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 areproposed. Experimental results have been carried out on di®erent realmatches of the Italian Serie A.1

Autore Pugliese

• Leo Marco , Spagnolo Paolo , Mazzeo Pier Luigi , D'orazio Tiziana Rita

Tutti gli autori

• P. Spagnolo; P. L. Mazzeo; ; M. Leo; T. D'Orazio

Titolo volume/Rivista

Lecture notes in computer science

Anno di pubblicazione

2010

ISSN

0302-9743

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

Non Disponibile