Exploring Deep Learning in Cricket: A Shot Detection and Analyzing Techniques

Abstract

Using deep learning techniques, this research study offers a novel method for analyzing cricket shots. The goal of the project is to build a solid framework that can reliably classify and examine different kinds of cricket shots under various playing circumstances and player styles. The technique begins with gathering a large dataset of cricket shot films, which is then rigorously preprocessed to annotate different sorts of shots. The next step involves choosing and training a Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) model to identify and categorize cricket shots according to their visual attributes. The principal aims of this study are as follows: (1) Construct a deep learning model that can reliably categorize cricket strokes; (2) Assess the model's performance through extensive testing and contrast with conventional techniques and (3) Use deep learning techniques to improve your analysis of cricket shots. This research is important because it has the potential to transform cricket performance evaluation and sports analytics. This study intends to use deep learning techniques to offer more accurate and thorough insights into cricket shot execution, assisting players, coaches, and analysts in making strategic decisions and optimizing performance.