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A cloud computing based Big-Bang Big-Crunch fuzzy logic multi classifier system for Soccer video scenes classification

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Abstract

Soccer video summarization and classification is becoming a very important topic due to the world wide importance and popularity of soccer games which drives the need to automatically classify video scenes thus enabling better sport analysis, refereeing, training, advertisement, etc. Machine learning has been applied to the task of sports video classification. However, for some specific image and video problems (like sports video scenes classification), the learning task becomes convoluted and difficult due to the dynamic nature of the video sequence and the associated uncertainties relating to changes in light conditions, background, camera angle, occlusions and indistinguishable scene features, etc. The majority of previous techniques (such as SVM, neural network, decision tree, etc.) applied to sports video classifications did not provide a consummate solution, and such models could not be easily understood by human users; meanwhile, they increased the complexity and time of computation and the associated costs of the involved standalone machines. Hence, there is a need to develop a system which is able to address these drawbacks and handle the high levels of uncertainty in video scenes classification and undertake the heavy video processing securely and efficiently on a cloud computing based instance. Hence, in this paper we present a cloud computing based multi classifier systems which aggregates three classifiers based on neural networks and two fuzzy logic classifiers based on type-1 fuzzy logic and type-2 fuzzy logic classification systems which were optimized by a Big-Bang Big crunch optimization to maximize the system performance. We will present several real world experiments which shows the proposed classification system operating in real-time to produce high classification accuracies for soccer videos which outperforms the standalone classification systems based on neural networks, type-1 and type-2 fuzzy logic systems.

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Notes

  1. PriceWaterhouseCoopers (2015). Changing the game outlook for the global sports market [online]. Available at: https://www.pwc.com/gx/en/hospitality-leisure/pdf/changing-the-game-outlook-for-the-global-sports-market-to-2015.pdf [last accessed 15/10/2015].

  2. Qualys Guard: https://www.qualys.com/enterprises/qualysguard/.

  3. Open Security Architecture, 2009, http://www.opensecurityarchitecture.org/cms/.

  4. UK Premier League (2015). Chelsea vs. Man City, 31st Jan 2015; Chelsea vs. Liverpool, 28th Jan 2015; Manchester City vs. West Bromwich Albion, 4th May 2015 [online]. Available at: http://www.zhiboba.cc/zuqiu.html [last accessed 15/5/2015].

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Authors and Affiliations

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

    Song Wei & Hani Hagras

  2. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Daniyal Alghazzawi

Authors
  1. Song Wei
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  2. Hani Hagras
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  3. Daniyal Alghazzawi
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Correspondence to Hani Hagras.

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Wei, S., Hagras, H. & Alghazzawi, D. A cloud computing based Big-Bang Big-Crunch fuzzy logic multi classifier system for Soccer video scenes classification. Memetic Comp. 8, 307–323 (2016). https://doi.org/10.1007/s12293-016-0207-0

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