Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots
Artificial Intelligence has become the new powerhouse of data analytics in this technological era. With advent of different Machine Learning and Computer Vision algorithms, applying them in data analytics has become a common trend. However, applying Deep Neural Networks in different sport data analyzing tasks and study the performance of these models is yet to be explored. Hence, in this paper, we have proposed a 13 layered Convolutional Neural Network referred as “Shot-Net” in order to classifying six categories of cricket shots, namely Cut Shot, Cover Drive, Straight Drive, Pull Shot, Scoop Shot and Leg Glance Shot. Our proposed model has achieved fairly high accuracy with low cross-entropy rate.
Cricket shot classification, Convolution neural network, Deep learning
Md. Ferdouse Ahmed Foysal, Mohammad Shakirul Islam, Asif Karim, Nafis Neehal