Shot-Net

A Convolutional Neural Network for Classifying Different Cricket Shots

Md Ferdouse Ahmed Foysal, Mohammad Shakirul Islam, Asif Karim, Nafis Neehal

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in ProceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationRecent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers
EditorsRavindra S. Hegadi, K. C. Santosh
PublisherSpringer-Verlag London Ltd.
Pages111-120
Number of pages10
ISBN (Print)9789811391804
DOIs
Publication statusPublished - 2019
Event2nd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018 - Solapur, India
Duration: 21 Dec 201822 Dec 2018

Publication series

NameCommunications in Computer and Information Science
Volume1035
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference2nd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018
CountryIndia
CitySolapur
Period21/12/1822/12/18

Fingerprint

Neural Networks
Neural networks
Sports
Computer vision
Artificial intelligence
Learning systems
Cross-entropy
Entropy
Computer Vision
Straight
Artificial Intelligence
Machine Learning
High Accuracy
Cover
Model
Deep neural networks
Trends

Cite this

Foysal, M. F. A., Islam, M. S., Karim, A., & Neehal, N. (2019). Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots. In R. S. Hegadi, & K. C. Santosh (Eds.), Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers (pp. 111-120). (Communications in Computer and Information Science; Vol. 1035). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-981-13-9181-1_10
Foysal, Md Ferdouse Ahmed ; Islam, Mohammad Shakirul ; Karim, Asif ; Neehal, Nafis. / Shot-Net : A Convolutional Neural Network for Classifying Different Cricket Shots. Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers. editor / Ravindra S. Hegadi ; K. C. Santosh. Springer-Verlag London Ltd., 2019. pp. 111-120 (Communications in Computer and Information Science).
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title = "Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots",
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 = "Convolution neural network, Cricket shot classification, Deep learning",
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Foysal, MFA, Islam, MS, Karim, A & Neehal, N 2019, Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots. in RS Hegadi & KC Santosh (eds), Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers. Communications in Computer and Information Science, vol. 1035, Springer-Verlag London Ltd., pp. 111-120, 2nd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018, Solapur, India, 21/12/18. https://doi.org/10.1007/978-981-13-9181-1_10

Shot-Net : A Convolutional Neural Network for Classifying Different Cricket Shots. / Foysal, Md Ferdouse Ahmed; Islam, Mohammad Shakirul; Karim, Asif; Neehal, Nafis.

Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers. ed. / Ravindra S. Hegadi; K. C. Santosh. Springer-Verlag London Ltd., 2019. p. 111-120 (Communications in Computer and Information Science; Vol. 1035).

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in ProceedingsResearchpeer-review

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AB - 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.

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Foysal MFA, Islam MS, Karim A, Neehal N. Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots. In Hegadi RS, Santosh KC, editors, Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers. Springer-Verlag London Ltd. 2019. p. 111-120. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-13-9181-1_10