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dc.contributor.authorSiddiqui, Hafeez Ur Rehman
dc.contributor.authorYounas, Faizan
dc.contributor.authorRustam, Furqan
dc.contributor.authorSoriano Flores, Emmanuel
dc.contributor.authorBrito Ballester, Julien
dc.contributor.authorTorre Díez, Isabel de la 
dc.contributor.authorDudley, Sandra
dc.contributor.authorAshraf, Imran
dc.date.accessioned2024-03-08T12:15:55Z
dc.date.available2024-03-08T12:15:55Z
dc.date.issued2023
dc.identifier.citationSensors, 2023, Vol. 23, Nº. 15, 6839es
dc.identifier.issn1424-8220es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/66573
dc.descriptionProducción Científicaes
dc.description.abstractCricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCricketes
dc.subjectSportses
dc.subjectDeportees
dc.subjectStrokees
dc.subjectPredictiones
dc.subjectPredicciónes
dc.subjectComputer visiones
dc.subjectVisión artificial (Robótica)es
dc.subjectMachine learninges
dc.subjectAprendizaje automáticoes
dc.subjectRandom forestes
dc.subjectAlgorithmses
dc.subjectAlgoritmoses
dc.subjectComputer sciencees
dc.subjectArtificial intelligencees
dc.titleEnhancing cricket performance analysis with human pose estimation and machine learninges
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2023 The authorses
dc.identifier.doi10.3390/s23156839es
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/23/15/6839es
dc.identifier.publicationfirstpage6839es
dc.identifier.publicationissue15es
dc.identifier.publicationtitleSensorses
dc.identifier.publicationvolume23es
dc.peerreviewedSIes
dc.identifier.essn1424-8220es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco1203.04 Inteligencia Artificiales
dc.subject.unesco1203.17 Informáticaes


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