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Active Foreground Region Extraction and Tracking for Sports Video Annotation

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Abstract

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. 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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Notes

  1. http://www.cvg.cs.rdg.ac.uk/VS/.

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Authors and Affiliations

  1. School of Electronics and Computer Science, University of Westminster, 115 New Cavendish Street, Westminster, W1W 6UW, United Kingdom

    Markos Mentzelopoulos, Alexandra Psarrou & Anastassia Angelopoulou

  2. Department of Computer Technology and Computation, University of Alicante, Alicante, Spain

    José García-Rodríguez

Authors
  1. Markos Mentzelopoulos
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  2. Alexandra Psarrou
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  3. Anastassia Angelopoulou
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  4. José García-Rodríguez
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Correspondence to Markos Mentzelopoulos.

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Mentzelopoulos, M., Psarrou, A., Angelopoulou, A. et al. Active Foreground Region Extraction and Tracking for Sports Video Annotation. Neural Process Lett 37, 33–46 (2013). https://doi.org/10.1007/s11063-012-9267-4

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  • DOI: https://doi.org/10.1007/s11063-012-9267-4

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