TY - GEN
T1 - The Classification of Skateboarding Trick Manoeuvres Through the Integration of Image Processing Techniques and Machine Learning
AU - Shapiee, Muhammad Nur Aiman
AU - Ibrahim, Muhammad Ar Rahim
AU - Mohd Razman, Mohd Azraai
AU - Abdullah, Muhammad Amirul
AU - Musa, Rabiu Muazu
AU - Hassan, Mohd Hasnun Arif
AU - Abdul Majeed, Anwar P.P.
N1 - Funding Information:
Acknowledgements The authors would like to gratefully acknowledge Universiti Malaysia Pahang for supporting this study via RDU190328.
Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - More often than not, the evaluation of skateboarding tricks executions is assessed intuitively according to the judges’ observation and hence are susceptible to biasness if not inaccurate judgement. Hence, it is crucial to underline the benchmark for analyzing the rate of successful execution of skateboarding trick for high level tournaments. The common tricks in skateboarding such as Kickflip, Ollie, Nollie, Pop Shove-it and Frontside 180 are investigated in this study via the synthetization of image processing and machine learning classifiers. The subject used for accomplishing the tricks is a male amateur skateboarder at the age of 23 years old with ±5.0 years’ experience using ORY skateboard. Each trick is collected upon five successful landings and the camera is placed 1.26 m from the subject on a flat cemented ground. The features extracted from each trick were engineered using Inception-V3 image embedder. Several classification models were evaluated, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Logistic Regression (LR), Random Forest (RF) and Naïve Bayes (NB) on their ability in classifying the tricks based on the engineered features. It was observed from the preliminary investigation that the SVM model attained the highest classification accuracy with a value of 99.5% followed by LR, k-NN, RF, and NB with 98.6%, 95.8%, 82.4% and 78.7%, respectively. It could be inferred that the method proposed decisively provide the classification of skateboarding tricks efficiently and would certainly provide a more objective based judgment in awarding the score of the tricks.
AB - More often than not, the evaluation of skateboarding tricks executions is assessed intuitively according to the judges’ observation and hence are susceptible to biasness if not inaccurate judgement. Hence, it is crucial to underline the benchmark for analyzing the rate of successful execution of skateboarding trick for high level tournaments. The common tricks in skateboarding such as Kickflip, Ollie, Nollie, Pop Shove-it and Frontside 180 are investigated in this study via the synthetization of image processing and machine learning classifiers. The subject used for accomplishing the tricks is a male amateur skateboarder at the age of 23 years old with ±5.0 years’ experience using ORY skateboard. Each trick is collected upon five successful landings and the camera is placed 1.26 m from the subject on a flat cemented ground. The features extracted from each trick were engineered using Inception-V3 image embedder. Several classification models were evaluated, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Logistic Regression (LR), Random Forest (RF) and Naïve Bayes (NB) on their ability in classifying the tricks based on the engineered features. It was observed from the preliminary investigation that the SVM model attained the highest classification accuracy with a value of 99.5% followed by LR, k-NN, RF, and NB with 98.6%, 95.8%, 82.4% and 78.7%, respectively. It could be inferred that the method proposed decisively provide the classification of skateboarding tricks efficiently and would certainly provide a more objective based judgment in awarding the score of the tricks.
KW - Classification
KW - Image processing
KW - Machine learning
KW - Skateboarding tricks
UR - http://www.scopus.com/inward/record.url?scp=85083081023&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-2317-5_29
DO - 10.1007/978-981-15-2317-5_29
M3 - Conference Proceeding
AN - SCOPUS:85083081023
SN - 9789811523168
T3 - Lecture Notes in Electrical Engineering
SP - 347
EP - 356
BT - InECCE 2019 - Proceedings of the 5th International Conference on Electrical, Control and Computer Engineering
A2 - Kasruddin Nasir, Ahmad Nor
A2 - Saari, Mohd Mawardi
A2 - Daud, Mohd Razali
A2 - Mohd Faudzi, Ahmad Afif
A2 - Ahmad, Mohd Ashraf
A2 - Najib, Muhammad Sharfi
A2 - Abdul Wahab, Yasmin
A2 - Othman, Nur Aqilah
A2 - Abd Ghani, Nor Maniha
A2 - Irawan, Addie
A2 - Khatun, Sabira
A2 - Raja Ismail, Raja Mohd Taufika
PB - Springer
T2 - 5th International Conference on Electrical, Control and Computer Engineering, InECCE 2019
Y2 - 29 July 2019 through 29 July 2019
ER -