Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning

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

Classifying and learning cricket shots using camera motion

This paper presents a method to classify and learn cricket shots. The procedure begins by extracting the camera motion parameters from the shots. Then the camera parameter values are converted to symbolic form and combined to generate a symbolic description that defines the trajectory of the cricket bell. The description generated is used to classify the cricket shot and to dynamically expand or update the system\u27s knowledge of shots. The first novel aspect of this approach is that by using the camera motion parameters, a complex and difficult process of low level image segmenting of either the batsman or the cricket ball from video images is avoided. Also the method does not require high resolution images. Another novel aspect of this work is the use of a new incremental learning algorithm that enables the system to improve and update its knowledge base. Unlike previously developed algorithms which store training instances and have simple method to prime their concept hierarchies, the incremental learning algorithm used in this work generates compact concept hierarchies and uses evidence based forgetting. The results show that the system performs well in the task of classifying four types of cricket shots.<br /