The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models
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 skateboardi...
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2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/30736/1/978-981-15-6025-5_20 http://umpir.ump.edu.my/id/eprint/30736/ https://doi.org/10.1007/978-981-15-6025-5_20 https://doi.org/10.1007/978-981-15-6025-5_20 |
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my.ump.umpir.307362021-02-23T07:48:28Z http://umpir.ump.edu.my/id/eprint/30736/ The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models Muhammad Nur Aiman, Shapiee Ibrahim, Muhammad Ar Rahim Mohd Azraai, Mohd Razman Muhammad Amirul, Abdullah Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering 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. Springer 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30736/1/978-981-15-6025-5_20 Muhammad Nur Aiman, Shapiee and Ibrahim, Muhammad Ar Rahim and Mohd Azraai, Mohd Razman and Muhammad Amirul, Abdullah and Musa, Rabiu Muazu and Anwar, P. P. Abdul Majeed (2020) The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models. In: Embracing Industry 4.0: Selected Articles from MUCET 2019, 19-22 November 2019 , Kuantan, Pahang, Malaysia. pp. 219-226., 678. ISSN 1876-1100 https://doi.org/10.1007/978-981-15-6025-5_20 https://doi.org/10.1007/978-981-15-6025-5_20 |
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TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
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TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Muhammad Nur Aiman, Shapiee Ibrahim, Muhammad Ar Rahim Mohd Azraai, Mohd Razman Muhammad Amirul, Abdullah Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models |
| description |
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. |
| format |
Conference or Workshop Item |
| author |
Muhammad Nur Aiman, Shapiee Ibrahim, Muhammad Ar Rahim Mohd Azraai, Mohd Razman Muhammad Amirul, Abdullah Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed |
| author_facet |
Muhammad Nur Aiman, Shapiee Ibrahim, Muhammad Ar Rahim Mohd Azraai, Mohd Razman Muhammad Amirul, Abdullah Musa, Rabiu Muazu Anwar, P. P. Abdul Majeed |
| author_sort |
Muhammad Nur Aiman, Shapiee |
| title |
The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models |
| title_short |
The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models |
| title_full |
The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models |
| title_fullStr |
The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models |
| title_full_unstemmed |
The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models |
| title_sort |
classification of skateboarding tricks by means of the integration of transfer learning and machine learning models |
| publisher |
Springer |
| publishDate |
2020 |
| url |
http://umpir.ump.edu.my/id/eprint/30736/1/978-981-15-6025-5_20 http://umpir.ump.edu.my/id/eprint/30736/ https://doi.org/10.1007/978-981-15-6025-5_20 https://doi.org/10.1007/978-981-15-6025-5_20 |
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1692991972047847424 |
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13.211869 |