Human action recognition using support vector machines and 3D convolutional neural networks
Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN ap...
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| Format: | EJournal Article |
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Universitas Ahmad Dahlan,
2017-03-31.
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| LEADER | 02093 am a22002893u 4500 | ||
|---|---|---|---|
| 001 | 0 nhttps:__ijain.org_index.php_IJAIN_article_downloadSuppFile_89_24 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Latah, Majd |e author |
| 100 | 1 | 0 | |e contributor |
| 245 | 0 | 0 | |a Human action recognition using support vector machines and 3D convolutional neural networks |
| 260 | |b Universitas Ahmad Dahlan, |c 2017-03-31. | ||
| 500 | |a https://ijain.org/index.php/IJAIN/article/view/89 | ||
| 520 | |a Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet the limited memory constraints. The proposed architecture was trained and evaluated on KTH action recognition dataset and achieved a good performance. | ||
| 540 | |a Copyright (c) 2017 Majd Latah | ||
| 540 | |a https://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a 3D Convolutional Neural Network (CNN); Human Action Recognition; Support Vector Machines (SVM) | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n International Journal of Advances in Intelligent Informatics; Vol 3, No 1 (2017): March 2017; 47-55 | |
| 786 | 0 | |n 2548-3161 | |
| 786 | 0 | |n 2442-6571 | |
| 787 | 0 | |n https://ijain.org/index.php/IJAIN/article/view/89/ijain_v3i2_p47-55 | |
| 787 | 0 | |n https://ijain.org/index.php/IJAIN/article/downloadSuppFile/89/24 | |
| 856 | 4 | 1 | |u https://ijain.org/index.php/IJAIN/article/view/89/ijain_v3i2_p47-55 |z Get Fulltext |
| 856 | 4 | 1 | |u https://ijain.org/index.php/IJAIN/article/downloadSuppFile/89/24 |z Get Fulltext |