The classification of skateboarding tricks : A transfer learning and machine learning approach
The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33627/1/The%20classification%20of%20skateboarding%20tricks%20_%20a%20transfer%20learning.pdf |
| _version_ | 1825814243886235648 |
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| author | Muhammad Nur Aiman, Shapiee Muhammad Ar Rahim, Ibrahim Muhammad Amirul, Abdullah Rabiu Muazu, Musa Noor Azuan, Abu Osman Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman |
| author_facet | Muhammad Nur Aiman, Shapiee Muhammad Ar Rahim, Ibrahim Muhammad Amirul, Abdullah Rabiu Muazu, Musa Noor Azuan, Abu Osman Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman |
| author_sort | Muhammad Nur Aiman, Shapiee |
| collection | UMP |
| description | The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision. |
| first_indexed | 2024-03-06T12:55:54Z |
| format | Article |
| id | UMPir33627 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:55:54Z |
| publishDate | 2020 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir336272022-04-05T06:54:26Z http://umpir.ump.edu.my/id/eprint/33627/ The classification of skateboarding tricks : A transfer learning and machine learning approach Muhammad Nur Aiman, Shapiee Muhammad Ar Rahim, Ibrahim Muhammad Amirul, Abdullah Rabiu Muazu, Musa Noor Azuan, Abu Osman Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman TJ Mechanical engineering and machinery TS Manufactures The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision. Penerbit UMP 2020 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33627/1/The%20classification%20of%20skateboarding%20tricks%20_%20a%20transfer%20learning.pdf Muhammad Nur Aiman, Shapiee and Muhammad Ar Rahim, Ibrahim and Muhammad Amirul, Abdullah and Rabiu Muazu, Musa and Noor Azuan, Abu Osman and Anwar P. P., Abdul Majeed and Mohd Azraai, Mohd Razman (2020) The classification of skateboarding tricks : A transfer learning and machine learning approach. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 2 (2). pp. 1-12. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v2i2.6683 https://doi.org/10.15282/mekatronika.v2i2.6683 |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Muhammad Nur Aiman, Shapiee Muhammad Ar Rahim, Ibrahim Muhammad Amirul, Abdullah Rabiu Muazu, Musa Noor Azuan, Abu Osman Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman The classification of skateboarding tricks : A transfer learning and machine learning approach |
| title | The classification of skateboarding tricks : A transfer learning and machine learning approach |
| title_full | The classification of skateboarding tricks : A transfer learning and machine learning approach |
| title_fullStr | The classification of skateboarding tricks : A transfer learning and machine learning approach |
| title_full_unstemmed | The classification of skateboarding tricks : A transfer learning and machine learning approach |
| title_short | The classification of skateboarding tricks : A transfer learning and machine learning approach |
| title_sort | classification of skateboarding tricks a transfer learning and machine learning approach |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/33627/1/The%20classification%20of%20skateboarding%20tricks%20_%20a%20transfer%20learning.pdf |
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