TY - GEN
T1 - The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models
AU - Shapiee, Muhammad Nur Aiman
AU - Ibrahim, Muhammad Ar Rahim
AU - Razman, Mohd Azraai Mohd
AU - Abdullah, Muhammad Amirul
AU - Musa, Rabiu Muazu
AU - Abdul Majeed, Anwar P.P.
N1 - Funding Information:
The authors would like to acknowledge the Ministry of Education, Malaysia and Universiti Malaysia Pahang for supporting and funding this research via FRGS/1/2019/TK03/UMP/02/6 (RDU1901115).
Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - Generally, the assessment of skateboarding tricks executions is completed abstractly dependent on the judges’ understanding and experience. Hence, an objective and means for assessing skateboarding tricks, especially in the big competition are important. This research aims at classifying skateboarding flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie Front Shove and Frontside 180 through camera vision and pre-trained convolution neural network for feature extraction coupled with a conventional machine learning model. An amateur skateboarder (23 years of age ± 5.0 years’ experience) executed five tricks for each type of trick repeatedly on a skateboard from a camera with a distance of 1.26 m. From the images captured, the features were engineered and extracted through Transfer Learning, particularly VGG-16 and then classified by means of Logistic Regression (LR) and k-Nearest Neighbour (k-NN) models. The observation from the preliminary investigation demonstrated that through the proposed methodology, the LR and k-NN models attained a classification accuracy of 99.1% and 97.7%, on the test dataset, respectively. It could be shown that the proposed strategy can classify the skateboard tricks well and would, in the long run, support the judges in providing an increasingly objective-based judgment.
AB - Generally, the assessment of skateboarding tricks executions is completed abstractly dependent on the judges’ understanding and experience. Hence, an objective and means for assessing skateboarding tricks, especially in the big competition are important. This research aims at classifying skateboarding flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie Front Shove and Frontside 180 through camera vision and pre-trained convolution neural network for feature extraction coupled with a conventional machine learning model. An amateur skateboarder (23 years of age ± 5.0 years’ experience) executed five tricks for each type of trick repeatedly on a skateboard from a camera with a distance of 1.26 m. From the images captured, the features were engineered and extracted through Transfer Learning, particularly VGG-16 and then classified by means of Logistic Regression (LR) and k-Nearest Neighbour (k-NN) models. The observation from the preliminary investigation demonstrated that through the proposed methodology, the LR and k-NN models attained a classification accuracy of 99.1% and 97.7%, on the test dataset, respectively. It could be shown that the proposed strategy can classify the skateboard tricks well and would, in the long run, support the judges in providing an increasingly objective-based judgment.
KW - Classification
KW - Image processing
KW - Machine learning
KW - Skateboarding tricks
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85088557033&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-6025-5_20
DO - 10.1007/978-981-15-6025-5_20
M3 - Conference Proceeding
AN - SCOPUS:85088557033
SN - 9789811560248
T3 - Lecture Notes in Electrical Engineering
SP - 219
EP - 226
BT - Embracing Industry 4.0 - Selected Articles from MUCET 2019
A2 - Mohd Razman, Mohd Azraai
A2 - Mat Jizat, Jessnor Arif
A2 - Mat Yahya, Nafrizuan
A2 - Myung, Hyun
A2 - Zainal Abidin, Amar Faiz
A2 - Abdul Karim, Mohamad Shaiful
PB - Springer
T2 - 11th Malaysian Technical Universities Conference on Engineering and Technology, MUCET 2019
Y2 - 19 November 2019 through 22 November 2019
ER -