Recognition of badminton action using convolutional neural network

Abstract

Deep learning approach has become a research interest in action recognition application due to its ability to surpass the performance of conventional machine learning approaches. Convolutional Neural Network (CNN) is among the widely used architecture in most action recognition works. There are various models exist in CNN but no research has been done to analyse which model has the best performance in recognizing actions for badminton. Hence, in this paper, we are comparing the performance of four different established pre-trained models of deep CNN in classifying the badminton match images to recognize the different actions done by the athlete. Four models used for comparison are AlexNet, GoogleNet, VggNet-16 and VggNet-19. This experimental work categorized images into two classes: Hit and non-hit action. Firstly, each image frame was extracted from Yonex All England Man Single Match 2017 broadcast video. Then, the image frames were fed as the input to each classifier model for classification. Finally, the performance of each classifier model was evaluated by plotting its performance accuracy in the form of confusion matrix. The result shows that the GoogleNet model has the highest classification accuracy which is 87.5% compared to other models. In a conclusion, the pre-trained GoogleNet model is capable to be used in recognizing actions in badminton match which might be useful in badminton sport performance technology. The main contribution of this paper is that it provides an analysis of the performance of four different pre-trained deep CNN models in recognizing badminton actions which have not been done before by other researchers. Thus, the analysis will help in the future work to improve the existing deep learning models’ architecture for a better performance in badminton action recognition.