Application of computer vision in Cricket: Foot overstep no-ball detection

Chowdhury, Azm Ehtesham; Rahim, Shamsur; Rahman, Asif Ur

doi:10.1109/ceeict.2016.7873086

Cited by 17 publications

(5 citation statements)

References 7 publications

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“…Here, it summarizes the comparison among related works. Chowdhury et al [ 19 ] applied the image subtraction method to the pixel value for having the chance to make a judgment by further testing the system for 6 input frames using computer vision, where our proposed system is tested on 262 test images. Batra et al [ 20 ] introduced a no-ball detection system through the distance among bowling crease points, popping crease point, and foot marks of the bowlers using the contour algorithm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…They used visual words model to demonstrate the main frame for each individual shot which will go to SVM and PLSA to classify the main frame towards predicting the type of shot, as it is not domain specific and can be integrated with different types of games. Chowdhury et al [ 19 ] used computer vision to detect foot no ball in a cricket match which applied image subtraction method on the pixel value for having the chance to make a judgment. Despite the fact that several tracking methods for improved monitoring of crickets have been published in a number of articles, relatively few studies specifically address cricket no ball tracking.…”

Section: Related Workmentioning

confidence: 99%

“…(1) Chowdhury et al [19] applied the image subtraction method to the pixel value for having the chance to make a judgment by further testing the system for 6 input frames using computer vision, where our proposed system is tested on 262 test images.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

See 2 more Smart Citations

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: Experimental Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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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“…Quantitative findings showed that one of the key factors affecting match outcome is how well our algorithms forecast the number of runs scored. The study by A Z M Ehtesham Chowdhury et al ( 44 ) aimed at concretizing the decision regarding the occurrence or non-occurrence of the no ball. The study made two divisions of the bowling crease, the transformation in pixels then was calculated using the picture decrement technique on two areas.…”

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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“…Hari et al [5] used the referee's intensity projection profile to extract the highlight event of the match of cricket. To recognize foot crossings, Chowdhury et al [6] recommended using video processing technologies. Lazarescu et al [7] categorized cricket footage in broad terms based on camera movement metrics.…”

Section: Related Workmentioning

confidence: 99%

Cricket Event Recognition and Classification from Umpire Action Gestures using Convolutional Neural Network

Nandyal¹,

Kattimani²

2022

IJACSA

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The advancement of hardware and deep learning technologies has made it possible to apply these technologies to a variety of fields. A deep learning architecture, the Convolutional Neural Network (CNN), revolutionized the field of computer vision. One of the most popular applications of computer vision is in sports. There are different types of events in cricket, which makes it a complex game. This task introduces a new dataset called SNWOLF for detecting Umpire postures and categorizing events in cricket match. The proposed dataset will be a preliminary help, it was assessed in system development for the automatic generation of highlights from cricket sport. When it comes to cricket, the umpire has the authority to make crucial decisions about on-field incidents. The referee signals important incidents with hand signals and gestures that are one-of-a-kind. Based on detecting the referee's stance from the cricket video referee action frame, it identifies most frequently used events classification: SIX, NO BALL, WIDE, OUT, LEG BYE, and FOUR. The proposed method utilizes Convolutional Neural Networks (CNNs) architecture to extract features and classify identified frames into Umpire postures of six event classes. Here created a completely new dataset of 1040 images of Umpire Action Images containing these six events. Our method train CNNs classifier on 80% images of SNWOLF dataset and tested on 20% of remaining images. Our approach achieves an average overall accuracy of 98.20% and converges on very low crossentropy losses. The proposed system is a influential answer for generation of cricket sport highlights.

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Application of computer vision in Cricket: Foot overstep no-ball detection

Cited by 17 publications

References 7 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

Information communication and technology in sports: a meticulous review

Cricket Event Recognition and Classification from Umpire Action Gestures using Convolutional Neural Network

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