An unsupervised method for active region extraction in sports videos

Mentzelopoulos, M., Psarrou, A. and Angelopoulou, A. 2011. An unsupervised method for active region extraction in sports videos. in: Cabestany J., Rojas I. and Joya G. (ed.) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692 Springer. pp. 42-49

Chapter titleAn unsupervised method for active region extraction in sports videos
AuthorsMentzelopoulos, M., Psarrou, A. and Angelopoulou, A.
EditorsCabestany J., Rojas I. and Joya G.
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.

Book titleAdvances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692
Page range42-49
Year2011
PublisherSpringer
Publication dates
Published2011
ISBN9783642214974
9783642214981
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-642-21498-1_6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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