Abstract
In this paper, we propose an efficient video analysis framework to assign broadcast soccer video shots into their respective view classes, and then detect players in long view shots. Our technique is built on dominant color region based segmentation for soccer playfield extraction. A long-view shot classifier uses a combination of “grass-area” ratio and “top-grass” analysis. A player detector applies the distinctive uniform knowledge of interesting objects based on colors referring from the result of playfield. In order to verify the player region segmented using colour, we introduce the four-seed edge features which prune the redundant edges denoting the noise of court lines or audiences. The player detection performance is suitable to employ tracking methods in order to exploit higher semantic information from the games. Experimental evaluation of the framework is extensively demonstrated in numerous challenging test sequences of the 2010 FiFa World Cup South Africa. The results show the robustness of our framework, and the potential future-work.
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Tran, Q., Tran, A., Dinh, T.B., Duong, D. (2012). Long-View Player Detection Framework Algorithm in Broadcast Soccer Videos. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_72
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DOI: https://doi.org/10.1007/978-3-642-25944-9_72
Publisher Name: Springer, Berlin, Heidelberg
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