RT info:eu-repo/semantics/article
T1 Enhancing cricket performance analysis with human pose estimation and machine learning
A1 Siddiqui, Hafeez Ur Rehman
A1 Younas, Faizan
A1 Rustam, Furqan
A1 Soriano Flores, Emmanuel
A1 Brito Ballester, Julien
A1 Torre Díez, Isabel de la
A1 Dudley, Sandra
A1 Ashraf, Imran
K1 Cricket
K1 Sports
K1 Deporte
K1 Stroke
K1 Prediction
K1 Predicción
K1 Computer vision
K1 Visión artificial (Robótica)
K1 Machine learning
K1 Aprendizaje automático
K1 Random forest
K1 Algorithms
K1 Algoritmos
K1 Computer science
K1 Artificial intelligence
K1 1203.04 Inteligencia Artificial
K1 1203.17 Informática
AB 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.
PB MDPI
SN 1424-8220
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/66573
UL https://uvadoc.uva.es/handle/10324/66573
LA eng
NO Sensors, 2023, Vol. 23, Nº. 15, 6839
NO Producción Científica
DS UVaDOC
RD 27-dic-2024