Abstract
In order to perform automatic analysis of sport videos acquired 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 affect any high level semantic analysis. Supervised approaches for object classification, require the construction of ad hoc models before the processing and also a manual selection of different player patches belonging to the team classes. The idea of this paper is to collect the players patches coming from six different cameras, and after a pre-processing step based on CBTF (Cumulative Brightness Transfer Function) studying and comparing different unsupervised method for classification. The pre-processing step based on CBTF has been implemented in order to mitigate difference in appearance between images acquired by different cameras. We tested three different unsupervised classification algorithms (MBSAS - a sequential clustering algorithm; BCLS - a competitive one; and k-means - a hard-clustering algorithm) on the transformed patches. Results obtained by comparing different set of features with different classifiers are proposed. Experimental results have been carried out on different real matches of the Italian Serie A.
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Mazzeo, P.L., Spagnolo, P., Leo, M., D’Orazio, T. (2010). Football Players Classification in a Multi-camera Environment. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17691-3_14
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DOI: https://doi.org/10.1007/978-3-642-17691-3_14
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