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

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

doi:10.1007/978-981-13-9181-1_10

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 proceeding › Conference Paper published in Proceedings › peer-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 language	English
Title of host publication	Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers
Editors	Ravindra S. Hegadi, K. C. Santosh
Place of Publication	Singapore
Publisher	Springer-Verlag London Ltd.
Pages	111-120
Number of pages	10
Edition	1
ISBN (Electronic)	9789811391811
ISBN (Print)	9789811391804
DOIs	https://doi.org/10.1007/978-981-13-9181-1_10
Publication status	Published - 2019
Event	2nd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018 - Solapur, India Duration: 21 Dec 2018 → 22 Dec 2018

Publication series

Name	Communications in Computer and Information Science
Volume	1035
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	2nd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018
Country/Territory	India
City	Solapur
Period	21/12/18 → 22/12/18

Access to Document

10.1007/978-981-13-9181-1_10

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 (1 ed., 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. 1. ed. Singapore : 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",

author = "Foysal, {Md Ferdouse Ahmed} and Islam, {Mohammad Shakirul} and Asif Karim and Nafis Neehal",

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booktitle = "Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers",

address = "Germany",

edition = "1",

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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. 1 edn, Communications in Computer and Information Science, vol. 1035, Springer-Verlag London Ltd., Singapore, 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. 1. ed. Singapore : Springer-Verlag London Ltd., 2019. p. 111-120 (Communications in Computer and Information Science; Vol. 1035).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

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T1 - Shot-Net

T2 - 2nd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018

AU - Foysal, Md Ferdouse Ahmed

AU - Islam, Mohammad Shakirul

AU - Karim, Asif

AU - Neehal, Nafis

PY - 2019

Y1 - 2019

N2 - 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.

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.

KW - Convolution neural network

KW - Cricket shot classification

KW - Deep learning

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BT - Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers

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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. 1 ed. Singapore: 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

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

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A Comprehensive Framework for Clustering Ham and Spam Emails based upon Unsupervised Learning

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

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A Comprehensive Framework for Clustering Ham and Spam Emails based upon Unsupervised Learning

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