Shot-ViT: Cricket Batting Shots Classification with Vision Transformer Network

In the realm of computer vision applied to cricket analysis, classifying batting shots poses a formidable challenge, demanding nuanced comprehension and categorization. The classification of cricket shots is crucial as it empowers the players to strategically assess, adapt, and execute their game plans effectively, shaping the outcome of matches. This article introduces the Cricket Batting Shots Image dataset (CBSId), a newbenchmark dataset comprising 2160 meticulously annotatedcricket shot images across sevendistinct categories. The core objective of this research is to develop a robust system capable of effectively classifying cricket batting shots from images. To addressthis, we present a fine-tuned Vision Transformer-based model specifically adapted for cricket shot classification, termed Cricket Batting Shot Vision Transformer (Shot-ViT). Our proposed methodology demonstrates exceptional performance, achieving 92.58% validation accuracy on the CBSId. Shot-ViT notably outperforms established models such as VGG19, ResNet50, I-AlexNet, and ViT_B32 in cricket shot classification accuracy, showcasing the remarkable capabilities of Vision transformers in surpassing existing deep learning architectures for complex visual tasks. Vision transformers have the capacity to capture global context and long-range dependencies within images through self-attention mechanisms, enabling effective feature extraction and representation, which traditional models may struggle to achieve. The accurate classification of cricket batting shots holds profound implications for cricket coaching, player development, and match analysis. It has the potential to revolutionize training methodologies, providing players and coaches with precise insights into batting techniques and strategies and therebycontributingto the overall advancement of the sport.