A CNN-based approach to classify cricket bowlers based on their bowling actions

TL;DR

This paper presents a CNN-based method utilizing transfer learning with VGG16 to accurately identify cricket bowlers by their unique bowling actions, using a new dataset of 8100 images, achieving 93.3% accuracy.

Contribution

It introduces a novel CNN approach with transfer learning for bowler identification and creates a new dataset for this task.

Findings

Achieved 93.3% accuracy in bowler classification

Developed a new dataset of 8100 images of bowlers

Optimized transfer learning by freezing initial layers

Abstract

With the advances in hardware technologies and deep learning techniques, it has become feasible to apply these techniques in diverse fields. Convolutional Neural Network (CNN), an architecture from the field of deep learning, has revolutionized Computer Vision. Sports is one of the avenues in which the use of computer vision is thriving. Cricket is a complex game consisting of different types of shots, bowling actions and many other activities. Every bowler, in a game of cricket, bowls with a different bowling action. We leverage this point to identify different bowlers. In this paper, we have proposed a CNN model to identify eighteen different cricket bowlers based on their bowling actions using transfer learning. Additionally, we have created a completely new dataset containing 8100 images of these eighteen bowlers to train the proposed framework and evaluate its performance. We have used the VGG16 model pre-trained with the ImageNet dataset and added a few layers on top of it to build our model. After trying out different strategies, we found that freezing the weights for the first 14 layers of the network and training the rest of the layers works best. Our approach achieves an overall average accuracy of 93.3% on the test set and converges to a very low cross-entropy loss.