The classification of skateboarding tricks by means of the integration of transfer learning models and K-Nearest neighbors

Muhammad Nur Aiman, Shapiee and Muhammad Ar Rahim, Ibrahim and Mohd Azraai, Mohd Razman and Muhammad Amirul, Abdullah and Musa, Rabiu Muazu and Noor Azuan, Abu Osman and P.P. Abdul Majeed, Anwar (2022) The classification of skateboarding tricks by means of the integration of transfer learning models and K-Nearest neighbors. In: Lecture Notes in Electrical Engineering. Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 , 6 August 2020 , Gambang. pp. 439-450., 730. ISSN 1876-1100 ISBN 978-981334596-6 (Published)

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DOI/Official URL: https://doi.org/10.1007/978-981-33-4597-3_40

Abstract

The skateboarding scene has reached new heights, especially with its first appearance at the now postponed Tokyo Summer Olympic Games. Therefore, owing to the scale of the sport in such competitive games, advanced innovative assessment approaches have increasingly gained due attention by relevant stakeholders, especially with the interest of a more objective-based evaluation. We employed pre-trained Transfer Learning coupled with a fine-tuned k-Nearest Neighbor (k-NN) classifier to form several pipelines to investigate its efficacy in classifying skateboarding tricks, namely Kickflip, Pop Shove-it, Frontside 180, Ollie and Nollie Front Shove-it. From the five skateboarding tricks, a skateboarder would repeatedly perform it for five successful landed tricks captured by YI action camera. From that, the images would be feature engineered and extracted through five Transfer Learning models, namely VGG-16, VGG-19, DenseNet-121, DenseNet-201 and InceptionV3, then classified by employing the k-Nearest Neighbor (k-NN) classifier. It is demonstrated from the preliminary results, that the VGG-19 and DenseNet-201 pipeline, both attained a classification accuracy (CA) of 97% on the test dataset, followed by the DenseNet-121 and InceptionV3, in which both obtained a test CA of 96%. The least performing pipeline is the VGG-16, where a test CA of 94% is recorded. The result from the current study validated it could providing an objective judgment for judges in classifying skateboard tricks for the competition.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Classification; Image processing; Machine learning; Skateboarding tricks; Transfer learning
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	30 Oct 2024 04:25
Last Modified:	30 Oct 2024 04:25
URI:	http://umpir.ump.edu.my/id/eprint/42251
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