Football video annotation based on player motion recognition using enhanced entropy

  • Authors:

  • Markos Mentzelopoulos;Alexandra Psarrou;Anastassia Angelopoulou;José García-Rodríguez

  • Affiliations:

  • School of Electronics and Computer Science, University of Westminster, United Kingdom;School of Electronics and Computer Science, University of Westminster, United Kingdom;School of Electronics and Computer Science, University of Westminster, United Kingdom;Departamento de Tecnología Informática y Computación, Universidad de Alicante, Alicante, Spain

  • Venue:
  • IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
  • Year:
  • 2013

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Abstract

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy