Deep learning based cricket batting shot classification and performance analysis using computer vision

Cricket is a sport which is played and known worldwide. It requires proper technique and execution of shots by the batsman. This research aims on classifying different types of batting shots and analysing the player's performance using computer vision and deep learning techniques. It also includes comparison with the shots played by professional players. A detailed methodology explains about data collection, preprocessing, feature extraction, shot classification, and performance evaluation. Videos of players performing the batting shots is the source of data collection process. Optical flow calculation and pose estimation are the techniques used in the preprocessing phase to find the bat's motion, the player's position, and the shot's trajectory. The features are given to a 3D Convolutional Neural Network which recognizes and classifies the different types of shots. The classified shot types are further used for shot comparison and analysis, helping us to provide a detailed analysis of the player's performance. Moreover, the evaluation metrics and performance assessment are done by comparing with the shots played by top professionals that give the insights into the areas of improvement. The system is applicable in sports training, player performance analysis, and coaching, giving real-time insights and data-centric feedback for cricket players of all levels. Further developments could incorporate high-level biomechanics evaluation, real-time feedback implementation, and AI coaching support to allow for the further enhancement of batting performance.