Universiti Teknologi Malaysia Institutional Repository

Badminton player detection using faster region convolutional neural network

Rahmad, N. A. and Sufri, N. A. J. and Muzamil, N. H. and As'ari, M. A. (2019) Badminton player detection using faster region convolutional neural network. Indonesian Journal of Electrical Engineering and Computer Science, 14 (3). pp. 1330-1335. ISSN 2502-4752


Official URL: http://www.dx.doi.org/10.11591/ijeecs.v14.i3.pp133...


Nowadays, coaches and sport analyst are concerning about sport performance analysis through sport video match. However, they still used conventional method which is through manual observation of the full video that is very troublesome because they might miss some meaningful information presence in the video. Several previous studies have discussed about tracking ball movements, identification of player based on jersey color and number as well as player movement detection in various type of sport such as soccer and volleyball but not in badminton. Therefore, this study focused on developing an automated system using Faster Region Convolutional Neural Network (Faster R-CNN) to track the position of the badminton player from the sport broadcast video. In preparing the dataset for training and testing, several broadcast videos were converted into image frames before labelling the region which indicate the players. After that, several different trained Faster R-CNN detectors were produced from the dataset before tested with different set of videos to evaluate the detector performance. In evaluating the performance of each detector model, the average precision was obtained from precision recall graph. As a result, this study revealed that the detector successfully detects the player when the detector is being fed with more generalized dataset.

Item Type:Article
Uncontrolled Keywords:deep learning, faster r-cnn, player detection
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:91360
Deposited By: Narimah Nawil
Deposited On:30 Jun 2021 20:08
Last Modified:30 Jun 2021 20:08

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