TY  - JOUR
KW  - batsman stroke prediction; computer vision; machine learning; random forest
IS  - 15
Y1  - 2023/08//
UR  - http://doi.org/10.3390/s23156839
ID  - uninimx8653
SN  - 1424-8220
A1  - Siddiqui, Hafeez Ur Rehman
A1  - Younas, Faizan
A1  - Rustam, Furqan
A1  - Soriano Flores, Emmanuel
A1  - Brito Ballester, Julién
A1  - Diez, Isabel de la Torre
A1  - Dudley, Sandra
A1  - Ashraf, Imran
TI  - Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning
N2  - 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.
AV  - public
JF  - Sensors
VL  - 23
ER  -