Vision based automated badminton action recognition using the new local convolutional neural network extractor - Azmina Rahmad, N.; Amir As`ari, M.; Fauzi Ibrahim, M.; Jasmin Sufri, N. A.; Rangasamy, K. (MoHE 2019: Enhancing Health and Sports Performance by Design, 2019)
Performance analysis is essential in sports practice where the athlete is evaluated to improve their performance. Due to the rapid growth of science and technology, research on automated recognition of sports actions has become ubiquitous. The implementation of automated action recognition is an effort to overcome the manual action recognition in sport performance analysis. In this study, we developed a model for automated badminton action recognition from the computer vision data inputs using the deep learning pre-trained AlexNet Convolutional Neural Network (CNN) for features extraction and classify the features using supervised machine learning method which is linear Support-Vector Machine (SVM). The data inputs consist of badminton match images of two classes: hit and non-hit action. Before pre-trained AlexNet CNN was directly extracting the features, we introduced the new local CNN extractor in recognition pipeline. The results show that the classification accuracy with this new local CNN method achieved 98.7%. In conclusion, this new local CNN extractor can contribute to the improvement of the performance accuracy of the classification task.