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_aUMP _beng _cUMP _erda |
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| 090 | _aFTKPM .A36 2021 r Thesis | ||
| 100 | 1 |
_aMuhammad Nur Aiman Shapiee, _eauthor. |
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| 245 | 1 | 4 |
_aThe classification of skateboarding trick images by means of transfer learning and machine learning models / _cMuhammad Nur Aiman Shapiee |
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_aKuantan, Pahang : _bUMP, _c2021 |
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| 264 | 4 | _c© 2021 | |
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_axv, 129 pages : _billustrations (some color) ; _c30 cm. + _e1 CD ROM |
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_aunmediated _2rdamedia |
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_acomputer _2rdamedia |
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_atext file _bPDF _2rda |
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| 500 | _aFaculty of Manufacturing and Mechatronic Engineering Technology | ||
| 502 | _aThesis (Master of Science) -- Universiti Malaysia Pahang – 2021 | ||
| 504 | _aIncludes bibliographical references | ||
| 520 | 3 | _aThe evaluation of tricks executions in skateboarding is commonly executed manually and subjectively. The panels of judges often rely on their prior experience in identifying the effectiveness of tricks performance during skateboarding competitions. This technique of classifying tricks is deemed as not a practical solution for the evaluation of skateboarding tricks mainly for big competitions. Therefore, an objective and unbiased means of evaluating skateboarding tricks for analyzing skateboarder’s trick is nontrivial. This study aims at classifying flat ground tricks namely Ollie, Kickflip, Pop Shove-it, Nollie Frontside Shove-it, and Frontside 180 through the camera vision and the combination of Transfer Learning (TL) and Machine Learning (ML). An amateur skateboarder (23 years of age with ± 5.0 years’ experience) executed five tricks for each type of trick repeatedly on an HZ skateboard from a YI action camera placed at a distance of 1.26 m on a cemented ground. The features from the image obtained are extracted automatically via 18 TL models. The features extracted from the models are then fed into different tuned ML classifiers models, for instance, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF). The grid search optimization technique through five-fold cross-validation was used to tune the hyperparameters of the classifiers evaluated. The data (722 images) was split into training, validation, and testing with a stratified ratio of 60:20:20, respectively. The study demonstrated that VGG16 + SVM and VGG19 + RF attained classification accuracy (CA) of 100% and 98%, respectively on the test dataset, followed by VGG19 + k-NN and also DenseNet201 + k-NN that achieved a CA of 97%. In order to evaluate the developed pipelines, robustness evaluation was carried out via the form of independent testing that employed the augmented images (2250 images). It was found that VGG16 + SVM, VGG19 + k-NN, and DenseNet201 + RF (by average) are able to yield reasonable CA with 99%, 98%, and 97%, respectively. Conclusively, based on the robustness evaluation, it can be ascertained that the VGG16 + SVM pipeline able to classify the tricks exceptionally well. Therefore, from the present study, it has been demonstrated that the proposed pipelines may facilitate judges in providing a more accurate evaluation of the tricks performed as opposed to the traditional method that is currently applied in competitions | |
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_aFaculty of Manufacturing and Mechatronic Engineering Technology _cDissertations |
| 650 | 0 | _aUniversities and colleges | |
| 650 | 0 | _aTheses | |
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_2lcc _cTHESIS |
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