Skip to main content

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

Part of the Communications in Computer and Information Science book series (CCIS,volume 1035)


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.


  • Cricket shot classification
  • Convolution neural network
  • Deep learning

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-13-9181-1_10
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-981-13-9181-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.


  1. Collins, H., Evans, R.: Hawkeye second edition public understanding revised 07 clean (2008)

    Google Scholar 

  2. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet Classification with Deep Convolutional Neural Networks (2012)

    Google Scholar 

  3. Collins, H., Evans, R.: Hawkeye-final-submission (2012)

    Google Scholar 

  4. Shotton, J., Sharp, T., Kipman, A.: Real-time human pose recognition in parts from single depth images. Commun. ACM (2013).

  5. Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: people detection and articulated pose estimation. In: Computer Vision and Pattern, pp. 1014–1021 (2009)

    Google Scholar 

  6. Batra, N., Gupta, H., Yadav, N., Gupta, A., Yadav, A.: Implementation of augmented reality in cricket for ball tracking and automated decision making for no ball, pp. 316–321 (2014).

  7. Wikipedia, Different Cricket Shots. (cricket)

  8. Dixit, K., Balakrishnan, A.: Deep learning using CNN’s for ball-by-ball outcome classification in sports, report submission on the course of Convolutional Neural Networks for Visual Recognition, Stanford University (2016)

    Google Scholar 

  9. Yao, B., Fei-Fei, L.: Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities. IEEE (2010). 978-1-4244-6985-7/10/26.00

    Google Scholar 

  10. Chowdhury, A.Z.M.E., Jihan, A.U.: Classification of Cricket Shots Using Computer Vision (2014)

    Google Scholar 

  11. Angadi, S.A., Naik, V.: A shot boundary detection technique based on local color moments in YCbCr color space. In: CS and IT-CSCP 2012 (2012)

    Google Scholar 

  12. Kolekar, M.H., Palaniappan, K., Sengupta, S.: Semantic event detection and classification in cricket video sequence. In: 2008 Sixth Indian Conference on Computer Vision, Graphics and Image Processing, Bhubaneswar, pp. 382–389 (2008)

    Google Scholar 

  13. Islam, M.S., Foysal, F.A., Neehal, N., Karim, E., Hossain, S.A.: InceptB: a CNN based classification approach for recognizing traditional bengali games. In: ICACC-2018 (2018)

    CrossRef  Google Scholar 

  14. Patel, H.A., Thakore, D.G.: Moving object tracking using Kalman filter. IJCSMC 2(4), 326–332 (2013)

    Google Scholar 

  15. Zhu, G., Huang, Q., Xu, C., Xing, L., Gao, W., Yao, H.: Human behavior analysis for highlight ranking in broadcast racket sports video. IEEE Trans. Multimedia 9(6), 1167–1182 (2007)

    CrossRef  Google Scholar 

  16. Simonyan, A., Zisserman, K.: Very deep convolutional networks for large-scale image recognition (2014)

    Google Scholar 

  17. Rock, R.A., Gibbs, A., Carlos, P.H.: The 5 the Umpire: Cricket”s Edge Detection System (2012)

    Google Scholar 

  18. Forsyth, D.A., Brien, V.O.: Computer Vision: A Modern Approach, 2 edn, pp. 88–101 (2003)

    Google Scholar 

  19. Lowe, D.G.: Distinctive image features from scale invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Md. Ferdouse Ahmed Foysal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Foysal, M.F.A., Islam, M.S., Karim, A., Neehal, N. (2019). Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9180-4

  • Online ISBN: 978-981-13-9181-1

  • eBook Packages: Computer ScienceComputer Science (R0)