DeepGrip: Cricket Bowling Delivery Detection with Superior CNN Architectures

Rahman, Rifat; Rahman, Mehfuz A; Islam, Md. Saiful; Hasan, Mahady

doi:10.1109/icict50816.2021.9358572

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…As a result of this idea, smart sports such as smart cricket have become very important. Although we conducted a literature review and observed that there has been minimal deep learning research in cricket no-ball recognition, Rahman et al [ 8 ] developed a unique approach for identifying the kind of delivery from a bowler's finger grip while the bowler is making a delivery. They correctly classify bowlers' grips with 0.9875 accuracy.…”

Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Das

Mahmud

Islam

et al. 2023

Computational Intelligence and Neuroscience

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Automation in every part of life has become a frequent situation because of the rapid advancement of technology, mostly driven by AI technology, and has helped facilitate improved decision-making. Machine learning and the deep learning subset of AI provide machines with the capacity to make judgments on their own through a continuous learning process from vast amounts of data. To decrease human mistakes while making critical choices and to improve knowledge of the game, AI-based technologies are now being implemented in numerous sports, including cricket, football, basketball, and others. Out of the most globally popular games in the world, cricket has a stronghold on the hearts of its fans. A broad range of technologies are being discovered and employed in cricket by the grace of AI to make fair choices as a method of helping on-field umpires because cricket is an unpredictable game, anything may happen in an instant, and a bad judgment can dramatically shift the game. Hence, a smart system can end the controversy caused just because of this error and create a healthy playing environment. Regarding this problem, our proposed framework successfully provides an automatic no-ball detection with 0.98 accuracy which incorporates data collection, processing, augmentation, enhancement, modeling, and evaluation. This study starts with collecting data and later keeps only the main portion of bowlers’ end by cropping it. Then, image enhancement technique are implied to make the image data more clear and noise free. After applying the image processing technique, we finally trained and tested the optimized CNN. Furthermore, we have increased the accuracy by using several modified pretrained model. Here, in this study, VGG16 and VGG19 achieved 0.98 accuracy and we considered VGG16 as the proposed model as it outperformed considering recall value.

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Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Das

Mahmud

Islam

et al. 2023

Computational Intelligence and Neuroscience

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“…Classifying these illegal deliveries has been of interest within the cricket community to develop tools for automated umpiring. To that end, early work with ball tracking in augmented reality focused on classifying no-balls (Batra et al, 2014), but more recent work has shifted to broadly classifying deliveries either by player or grip using CNNs, which offer a more detailed picture of bowling strategy for organizations, broadcasters, and fans alike (Al Kumar et al, 2019;Rahman et al, 2021).…”

Section: Delivery and Strike Classificationmentioning

confidence: 99%

Innovating a Centuries-Old Sport: How Emerging Data Analytics Tools Are Redefining Cricket

Shah¹,

Saeed²

2023

Harvard Data Science Review

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“…The statistics indicate a decrease in pitch tempo, which is the difference in pitch speed between the two ends of the pitch, on a pitch with a concrete foundation. In this paper by Rahman R et al ( 37 ), a unique method for determining the delivery style from a bowler's finger grip during delivery was proposed. The primary goal of this research is to accurately classify bowlers' grips using the transfer learning models and the prototype CNN architecture.…”

Section: Literature Reviewmentioning

confidence: 99%

Information communication and technology in sports: a meticulous review

Reyaz

Ahamad

Naseem

et al. 2023

Front. Sports Act. Living

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IntroductionSports of all kinds even though have an alluring property of keeping their onlookers stuck to their place, the introduction of Technology, however, revolutionized it all together. Not only in legal sports but also the training and teaching methods have been reformed. The use of Information Communication and Technology (ICT) based technologies [Convolutional Neural Networks (CNN), Hawkeye, Computer vision, Artificial intelligence, etc.] has moderately increased the interactive nature of sports. Employing ICT-driven technologies have continuously been increasing performance levels due to which high effective performance levels have been achieved. In addition to offering information to the users, it also acts as a means for connecting and interacting with the remaining world. In this article, we provide a review of the studies considering the developments and impact of employing ICT technology on sports, especially cricket. The study has focussed on domain-specific developments in cricket sports: developments in the batting domain, bowling domain, and wicketkeeping as well.MethodsFor the study, the analysis has been done following the PRISMA guidelines.ResultsThe study found that even though the researchers have done justifiable work in employing technology in sports as a whole but the domain-specific contribution in sports like cricket is not at the level as is need of the hour. In addition to the mentioned domains in the study, the research should gain speed in other domains like domain-specific Talent Identification for both genders, different age groups, diverse sports, etc.Discussionundoubtedly, the sports domain is employing technology at a vast level but a few domains like sports talent identification especially related to the most famous games like cricket require an immediate and intense focus of the researchers. Since this domain is still carrying out a traditional coach-oriented approach. There is an acute need to revolutionize the domain by incorporating modern technologies into it.

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DeepGrip: Cricket Bowling Delivery Detection with Superior CNN Architectures

Cited by 12 publications

References 22 publications

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Deep Transfer Learning‐Based Foot No‐Ball Detection in Live Cricket Match

Innovating a Centuries-Old Sport: How Emerging Data Analytics Tools Are Redefining Cricket

Information communication and technology in sports: a meticulous review

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