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Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots

  • Md. Ferdouse Ahmed FoysalEmail author
  • Mohammad Shakirul Islam
  • Asif Karim
  • Nafis Neehal
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1035)

Abstract

Artificial Intelligence has become the new powerhouse of data analytics in this technological era. With advent of different Machine Learning and Computer Vision algorithms, applying them in data analytics has become a common trend. However, applying Deep Neural Networks in different sport data analyzing tasks and study the performance of these models is yet to be explored. Hence, in this paper, we have proposed a 13 layered Convolutional Neural Network referred as “Shot-Net” in order to classifying six categories of cricket shots, namely Cut Shot, Cover Drive, Straight Drive, Pull Shot, Scoop Shot and Leg Glance Shot. Our proposed model has achieved fairly high accuracy with low cross-entropy rate.

Keywords

Cricket shot classification Convolution neural network Deep learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Md. Ferdouse Ahmed Foysal
    • 1
    Email author
  • Mohammad Shakirul Islam
    • 1
  • Asif Karim
    • 2
  • Nafis Neehal
    • 1
  1. 1.Department of Computer Science and EngineeringDaffodil International UniversityDhakaBangladesh
  2. 2.College of Engineering and ITCharles Darwin UniversityDarwinAustralia

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