Enhance Cricket players’ Fielding Skills: A CNN and LSTM Based Feedback and Recommendation System

Abstract

Cricket is most popular sport in the Sri Lanka, However, most of the players lack of their fielding skills due to current coaching methods fail to offer personalized feedback to players. Coaches are focusing on batting and bowling only. Fielding also required personalized feedback for players’ skill improvement. Existing study primarily focused on batting, bowling analysis only. Which is leave a gap for fielding improvement. This project focusses on enhance cricket players’ fielding skills by developing a CNN and LSTM based feedback and recommendation system. This solution is designed to classify fielding techniques and provide actionable feedback to players. A custom dataset was created using YouTube videos and contributions from five participants. The dataset was built in focusing on basic fielding techniques, such as high catch, orthodox cup ,reverse cup, long barrier and short barrier. The proposed project use pose estimation for extract key points and data augmentation techniques to increase classification accuracy. The hybrid model achieved an accuracy of 93%. The result of the proposed solution indicates that developed model capable to detect cricket players fielding performance level. The project allows to users to upload or record videos for get analysis and feedback. The system processes videos to detect players techniques and measure their accuracy.