Chapter title | Fast image representation with GPU-based growing neural gas |
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Authors | Garcia-Rodriguez, J., Angelopoulou, A., Morell, V., Orts Escolano, S., Psarrou, A. and Garcia-Chamizo, J.M. |
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Editors | Cabestany, J., Rojas, I. and Joya, G. |
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Abstract | In this paper, we propose a fully automatic and computationally efficient algorithm for analysis of sports videos. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos. |
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Book title | Advances in Computational Intelligence: 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8-10, 2011, Proceedings, Part II |
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Year | 2011 |
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Publisher | Springer |
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Publication dates |
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Published | 2011 |
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Series | Lecture notes in computer science |
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ISBN | 9783642214974 |
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ISSN | 0302-9743 |
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Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-642-21498-1_8 |
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Journal citation | (6692), pp. 42-49 |
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Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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