Analysis of Cricket Shots and Dismissal of Batsmen

The field of computer vision has witnessed remarkable advancements in recent years, driven by the convergence of deep learning techniques and advancements in computer hardware. Convolutional Neural Networks (CNNs) have emerged as a pivotal technology in revolutionizing computer vision tasks, such as image classification, object detection, and image segmentation. The domain of sports, specifically cricket, has also experienced a surge in the adoption of CNNs. Consequently, this paper employs a CNN architecture to accurately classify different types of batting shots played by cricketers. The proposed methodology involves the extraction of video frames capturing the batsman in action, which are subsequently fed into a pre-trained CNN model. To enhance the model’s performance in shot classification, transfer learning is employed to fine-tune the pre-trained network. Additionally, the paper delves into the realm of dismissal prediction in cricket using computer vision. This prediction is based on analyzing the length of the delivered ball and the batsman’s position during the moment of delivery. By evaluating the accuracy of the prediction model for specific batsmen, a comprehensive analysis is provided to assess its effectiveness. The integration of deep learning and computer vision presents substantial potential for advancing cricket analysis. The outcomes of this study can also be extended to other sports, facilitating further enhancements in game analysis and thereby improving players performance.