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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Assfalg, J., Bestini, M., Colombo, C., Del Bimbo, A., Nunziati, W.: Semantic annotation of soccer videos: automatic highlights identification. CVIU 92, 285–305 (2003)
Beetz, M., Bandouch, J., Gedikli, S.: Camera-based observation of football games for analyzing multi-agent activities. In: AAMAS, pp. 42–49 (2006)
Ekin, A., Tekalp, A., Mehrotra, R.: Automatic soccer video analysis and summarization. IEEE Trans. on Image Processing 12, 796–807 (2003)
Hayet, J., Mathes, T., Czyz, J., Piater, J., Verly, J., Macq, B.: A modula multi-camera framework for team sports tracking. In: AVSS, pp. 493–498 (2005)
Mazzeo, P.L., Spagnolo, P., d’Orazio, T.: Object tracking by non-overlapping distributed camera network. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 516–527. Springer, Heidelberg (2009)
Naemura, N., Fukuda, A., Mizutani, Y., Izumi, Y., Tanaka, Y., Enami, K.: Morphological segmentation of sport scenes using color information. IEEE Tr. on Br. 46, 181–188 (2003)
Prosser, B., Gong, S., Xiang, T.: Multi camera matching using bi-directional cumulative brightness transfer functions. In: BMVC 2008 (2008)
Spagnolo, P., D’Orazio, T., Leo, M., Distante, A.: Moving object segmentation by background subtractionand temporal analysis. Im. Vis. Comp. 24(5), 411–423 (2006)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, London ISBN 0-12-686140-4
Vandenbroucke, N., Macaire, L., Postaire, J.: Color image segmentation by pixel classification in an adapted hybrid color space.application to soccer image analysis. CVIU 90, 190–216 (2003)
Xu, M., Orwell, J., Lowery, L., Thirde, D.: Architecture and algorithms for tracking football players with multiple cameras. IEE Proc. Vis. Im. and Sign. 152, 232–241 (2005)
Xu, Z., Shi, P.: Segmentation of players and team discrimination in soccer videos. IEEE int. Work. VLSI Design Video Tech., 121–212 (2005)
Yu, X., Leong, H., Lim, J., Tian, Q., Jiang, Z.: Team possession analysis for broadcast soccer video based on ball trajectory. In: ICICS-PCM, pp. 1811–1815 (2003)
Zhang, D., Shih-Fu, C.: Real-time view recognition and event detection for sports video. J. Vis. Commun. Image r. 15, 330–347 (2004)
D’Orazio, T., Leo, M., Spagnolo, P., Mazzeo, P.L., Mosca, N., Nitti, M., Distante, A.: An Investigation Into the Feasibility of Real-Time Soccer Offside Detection From a Multiple Camera System. J. Cir. Sys. Video 19(12), 1804–1818 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-17691-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17690-6
Online ISBN: 978-3-642-17691-3
eBook Packages: Computer ScienceComputer Science (R0)