The classification of skateboarding trick images by means of transfer learning and machine learning models

The 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 n...

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Main Author:	Muhammad Nur Aiman, Shapiee
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TA Engineering (General) Civil engineering (General) TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/34939/1/The%20classification%20of%20skateboarding%20trick%20images%20by%20means%20of%20transfer%20learning.ir.pdf
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spelling	my-ump-ir.349392022-08-17T03:42:40Z The classification of skateboarding trick images by means of transfer learning and machine learning models 2021-05 Muhammad Nur Aiman, Shapiee TA Engineering (General). Civil engineering (General) TS Manufactures The 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. 2021-05 Thesis http://umpir.ump.edu.my/id/eprint/34939/ http://umpir.ump.edu.my/id/eprint/34939/1/The%20classification%20of%20skateboarding%20trick%20images%20by%20means%20of%20transfer%20learning.ir.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Manufacturing and Mechatronic Engineering Technology
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General) TS Manufactures
spellingShingle	TA Engineering (General) Civil engineering (General) TS Manufactures Muhammad Nur Aiman, Shapiee The classification of skateboarding trick images by means of transfer learning and machine learning models
description	The 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.
format	Thesis
qualification_level	Master's degree
author	Muhammad Nur Aiman, Shapiee
author_facet	Muhammad Nur Aiman, Shapiee
author_sort	Muhammad Nur Aiman, Shapiee
title	The classification of skateboarding trick images by means of transfer learning and machine learning models
title_short	The classification of skateboarding trick images by means of transfer learning and machine learning models
title_full	The classification of skateboarding trick images by means of transfer learning and machine learning models
title_fullStr	The classification of skateboarding trick images by means of transfer learning and machine learning models
title_full_unstemmed	The classification of skateboarding trick images by means of transfer learning and machine learning models
title_sort	classification of skateboarding trick images by means of transfer learning and machine learning models
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Manufacturing and Mechatronic Engineering Technology
publishDate	2021
url	http://umpir.ump.edu.my/id/eprint/34939/1/The%20classification%20of%20skateboarding%20trick%20images%20by%20means%20of%20transfer%20learning.ir.pdf
_version_	1783732218066108416

The classification of skateboarding trick images by means of transfer learning and machine learning models

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