Automated badminton smash recognition using convolutional neural network on the vision based data

• Main Authors: Rahmad, N. A., As’ari, M. A., Soeed, K., Zulkapri, I.
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Q Science (General)
• Online Access: http://eprints.utm.my/id/eprint/92486/1/MuhammadAmirAsari2020_AutomatedBadmintonSmashRecognitionUsingConvolutional.pdf; http://eprints.utm.my/id/eprint/92486/; http://dx.doi.org/10.1088/1757-899X/884/1/012009

Sport performance analysis in sports practice cannot be separable. It is important to help coach analyse and improve the performance of their athletes through training or game session. Due to the advancement of technology nowadays, the notational analysis of the video content using various software packages has become possible. Unluckily, the coach needs to recognize the actions manually before doing further analysis. The purpose of this study is to formulate an automated system for badminton smash recognition on widely available broadcasted videos using pre-trained Convolutional Neural Network (CNN) method. Smash and other badminton actions such as clear, drop, lift and net from the video were used to formulate the CNN models. Therefore, two experiments were conducted in this study. The first experiment is the study on the performance between four different existing pre-trained models which is AlexNet, GoogleNet, Vgg-16 Net and Vgg-19 Net in recognizing five actions. The results show that the pre-trained AlexNet model has the highest performance accuracy and fastest training period among the other models. The second experiment is the study on the performance of two different pre-trained models which is AlexNet and GoogleNet to recognize smash and non-smash action only. The results show that the pre-trained GoogleNet model produces the best performance in recognizing smash action. In conclusion, pre-trained AlexNet model is suitable to be used to automatically recognize the five badminton actions while GoogleNet model is excellent at recognizing smash action from the broadcasted video for further notational analysis.