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The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data

Automated action recognition is useful for improving the performance of the athletes through notational analysis. The notational analysis is usually used by the coach or notational analyst to study the movement patterns, strategy and tactics. Therefore, action recognition is the main key before furt...

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Main Authors: Rahmad, N. A., As'ari, M. A.
Format: Conference or Workshop Item
Language:English
Published: Institute of Physics Publishing 2020
Subjects:
QM Human anatomy
Online Access:http://eprints.utm.my/id/eprint/91247/1/MuhammadAmirAs%60Ari2020_ThenewConvolutionalNeuralNetwork.pdf
http://eprints.utm.my/id/eprint/91247/
http://dx.doi.org/10.1088/1742-6596/1529/2/022021
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id my.utm.91247
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spelling my.utm.912472021-06-30T11:59:32Z http://eprints.utm.my/id/eprint/91247/ The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data Rahmad, N. A. As'ari, M. A. QM Human anatomy Automated action recognition is useful for improving the performance of the athletes through notational analysis. The notational analysis is usually used by the coach or notational analyst to study the movement patterns, strategy and tactics. Therefore, action recognition is the main key before further analysis can be done. This paper focused on developing an automated badminton action recognition using vision based dataset. 1496 badminton match image frames of 5 actions were studied - smash, clear, drop, net shot and lift. At first, the dataset was classified into 0.8:0.2 for training and testing the classification task by machine learning. Secondly, features of the training dataset were extracted using the Alexnet Convolutional Neural Network (CNN) model. In extracting the features, we introduced the new local feature extractor technique that extracts features at the fc8 layer. After collecting all the features at the fc8 layer, features were being classified by using machine learning classifier which is linear Support Vector Machine (SVM). The experiment was repeated using a normal global feature extractor technique. Lastly, both of the new local and global feature extractor techniques were repeated using GoogleNet CNN model to compare the performance between AlexNet and GoogleNet model. The results show that the new local feature extractor using AlexNet CNN model has the best performance accuracy which is 82.0%. Institute of Physics Publishing 2020 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/91247/1/MuhammadAmirAs%60Ari2020_ThenewConvolutionalNeuralNetwork.pdf Rahmad, N. A. and As'ari, M. A. (2020) The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data. In: 2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019, 25 - 27 November 2019, Bandung, Indonesia. http://dx.doi.org/10.1088/1742-6596/1529/2/022021 Volume , Issue nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019, 25 November 2019 - 27 November 2019
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QM Human anatomy
spellingShingle QM Human anatomy
Rahmad, N. A.
As'ari, M. A.
The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data
description Automated action recognition is useful for improving the performance of the athletes through notational analysis. The notational analysis is usually used by the coach or notational analyst to study the movement patterns, strategy and tactics. Therefore, action recognition is the main key before further analysis can be done. This paper focused on developing an automated badminton action recognition using vision based dataset. 1496 badminton match image frames of 5 actions were studied - smash, clear, drop, net shot and lift. At first, the dataset was classified into 0.8:0.2 for training and testing the classification task by machine learning. Secondly, features of the training dataset were extracted using the Alexnet Convolutional Neural Network (CNN) model. In extracting the features, we introduced the new local feature extractor technique that extracts features at the fc8 layer. After collecting all the features at the fc8 layer, features were being classified by using machine learning classifier which is linear Support Vector Machine (SVM). The experiment was repeated using a normal global feature extractor technique. Lastly, both of the new local and global feature extractor techniques were repeated using GoogleNet CNN model to compare the performance between AlexNet and GoogleNet model. The results show that the new local feature extractor using AlexNet CNN model has the best performance accuracy which is 82.0%.
format Conference or Workshop Item
author Rahmad, N. A.
As'ari, M. A.
author_facet Rahmad, N. A.
As'ari, M. A.
author_sort Rahmad, N. A.
title The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data
title_short The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data
title_full The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data
title_fullStr The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data
title_full_unstemmed The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data
title_sort new convolutional neural network (cnn) local feature extractor for automated badminton action recognition on vision based data
publisher Institute of Physics Publishing
publishDate 2020
url http://eprints.utm.my/id/eprint/91247/1/MuhammadAmirAs%60Ari2020_ThenewConvolutionalNeuralNetwork.pdf
http://eprints.utm.my/id/eprint/91247/
http://dx.doi.org/10.1088/1742-6596/1529/2/022021
_version_ 1705056686472953856
score 13.211869

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