Hockey activity recognition using pre-trained deep learning model
Keerthana Rangasamy,
Muhammad Amir As’ari,
Nur Azmina Rahmad,
Nurul Fathiah Ghazali
Affiliations
Keerthana Rangasamy
School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia; Corresponding author.
Muhammad Amir As’ari
School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia; Sport Innovation and Technology Center (SITC), Institute of Human Centered Engineering (IHCE), Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Nur Azmina Rahmad
School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Nurul Fathiah Ghazali
School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Activity recognition in sports is often complex task resulting from the rapid dynamic interaction within players. In this paper, pre-trained VGG-16, deep learning based hockey activity recognition model has been proposed. Own hockey dataset consisting of four main activity includes free hit, goal, penalty corner and long corner was constructed as there are no existing field hockey datasets available. Experimental results indicate that the pre-trained deep learning model generates comparative results on this challenging dataset by tweaking the hyperparameters of this pre-trained model.
