eprintid: 8653 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/86/53 datestamp: 2023-09-05 23:30:13 lastmod: 2023-09-05 23:30:14 status_changed: 2023-09-05 23:30:13 type: article metadata_visibility: show creators_name: Siddiqui, Hafeez Ur Rehman creators_name: Younas, Faizan creators_name: Rustam, Furqan creators_name: Soriano Flores, Emmanuel creators_name: Brito Ballester, Julién creators_name: Diez, Isabel de la Torre creators_name: Dudley, Sandra creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: emmanuel.soriano@uneatlantico.es creators_id: julien.brito@uneatlantico.es creators_id: creators_id: creators_id: title: Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: batsman stroke prediction; computer vision; machine learning; random forest abstract: Cricket 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. date: 2023-08 publication: Sensors volume: 23 number: 15 pagerange: 6839 id_number: doi:10.3390/s23156839 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s23156839 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Cricket 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. metadata Siddiqui, Hafeez Ur Rehman; Younas, Faizan; Rustam, Furqan; Soriano Flores, Emmanuel; Brito Ballester, Julién; Diez, Isabel de la Torre; Dudley, Sandra y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, emmanuel.soriano@uneatlantico.es, julien.brito@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning. Sensors, 23 (15). p. 6839. ISSN 1424-8220 document_url: http://repositorio.unib.org/id/eprint/8653/1/sensors-23-06839-v2.pdf