The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Authors

  • Muhammad Nur Aiman Shapiee Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Muhammad Ar Rahim Ibrahim Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Muhammad Amirul Abdullah Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Rabiu Muazu Musa Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Terengganu, Malaysia
  • Noor Azuan Abu Osman Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Terengganu, Malaysia
  • Anwar P.P Abdul Majeed Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang
  • Mohd Azraai Mohd Razman Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang

DOI:

https://doi.org/10.15282/mekatronika.v2i2.6683

Keywords:

Transfer learning, Machine learning, Classification, Skateboarding

Abstract

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.

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Published

2020-10-27

How to Cite

[1]
M. N. A. Shapiee, “The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach”, MEKATRONIKA, vol. 2, no. 2, pp. 1–12, Oct. 2020.

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