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A Hybrid Fuzzy Football Scenes Classification System for Big Video Data

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Multimodal Analytics for Next-Generation Big Data Technologies and Applications

Abstract

In this chapter, we introduce a novel system based on Hybrid Interval Type-2 Fuzzy Logic Classification Systems (IT2FLCS) that can deal with a large training set of complicated video sequences to extract the main scenes in a football match. Football video scenes present added challenges due to the existence of specific objects and events which have high similar features like audience and coaches as well as being constituted from a series of quickly changing and dynamic frames with small inter-frame variations. In addition, there is an added difficulty associated with the need to have light-weight video classification systems which can work in real time with the massive data sizes associated with video analysis applications. The proposed fuzzy-based system allows achieving relatively high classification accuracy with a small number of rules, thus increasing the system interpretability.

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Author information

Authors and Affiliations

  1. The Computational Intelligence Centre, School of Computer Science and Electronic Engineering, Colchester, UK

    Song Wei & Hani Hagras

Authors
  1. Song Wei
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  2. Hani Hagras
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Corresponding author

Correspondence to Song Wei .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

    Kah Phooi Seng

  2. School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia

    Li-minn Ang

  3. School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia

    Alan Wee-Chung Liew

  4. The University of Sydney Business School, University of Sydney, Sydney, NSW, Australia

    Junbin Gao

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Wei, S., Hagras, H. (2019). A Hybrid Fuzzy Football Scenes Classification System for Big Video Data. In: Seng, K., Ang, Lm., Liew, AC., Gao, J. (eds) Multimodal Analytics for Next-Generation Big Data Technologies and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-97598-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-97598-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97597-9

  • Online ISBN: 978-3-319-97598-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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