Real-time human action recognition using stacked sparse autoencoders
Objectives: In this paper, an automated real-time human and human-action detection system is developed using Histogram of Oriented Gradients (HOG) and Stacked Sparse Auto-encoders respectively. Methods: For human detection, a feature descriptor is trained using SVM classifier and then is used for i...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Indian Society for Education and Environment & Informatics Publishing Limited
2018
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/62341/ http://irep.iium.edu.my/62341/1/Real-Time%20Human%20Action%20Recognition.pdf |
| _version_ | 1848785828698914816 |
|---|---|
| author | Farooq, Adnan Mohammad, Emad U Din Ahmad Zarir, Abdullah Ismail, Amelia Ritahani Sulaiman, Suriani |
| author_facet | Farooq, Adnan Mohammad, Emad U Din Ahmad Zarir, Abdullah Ismail, Amelia Ritahani Sulaiman, Suriani |
| author_sort | Farooq, Adnan |
| building | IIUM Repository |
| collection | Online Access |
| description | Objectives: In this paper, an automated real-time human and human-action detection system is developed using Histogram
of Oriented Gradients (HOG) and Stacked Sparse Auto-encoders respectively. Methods: For human detection, a feature descriptor is trained using SVM classifier and then is used for identification of humans in the frames. Stacked Sparse autoencoders are a category of deep neural networks, and in the proposed work is used for the feature extraction of human actions from the human action video dataset. The extracted features represent a dictionary which is used to map the input and produce a linear combination, following that soft-max classification is applied to train the model. To reduce the computational complexity, input frames has been changed into binary temporal difference images and fed to the neural network. Analysis: The proposed model matched the other state of the art models applied for human-action recognition classification problems. Applications: The study reveals that using multiple layers can improve the classification performance: 75% with two-layers and 83% with three-layers model. |
| first_indexed | 2025-11-14T16:59:21Z |
| format | Article |
| id | iium-62341 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:59:21Z |
| publishDate | 2018 |
| publisher | Indian Society for Education and Environment & Informatics Publishing Limited |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-623412024-01-02T02:25:46Z http://irep.iium.edu.my/62341/ Real-time human action recognition using stacked sparse autoencoders Farooq, Adnan Mohammad, Emad U Din Ahmad Zarir, Abdullah Ismail, Amelia Ritahani Sulaiman, Suriani QA75 Electronic computers. Computer science Objectives: In this paper, an automated real-time human and human-action detection system is developed using Histogram of Oriented Gradients (HOG) and Stacked Sparse Auto-encoders respectively. Methods: For human detection, a feature descriptor is trained using SVM classifier and then is used for identification of humans in the frames. Stacked Sparse autoencoders are a category of deep neural networks, and in the proposed work is used for the feature extraction of human actions from the human action video dataset. The extracted features represent a dictionary which is used to map the input and produce a linear combination, following that soft-max classification is applied to train the model. To reduce the computational complexity, input frames has been changed into binary temporal difference images and fed to the neural network. Analysis: The proposed model matched the other state of the art models applied for human-action recognition classification problems. Applications: The study reveals that using multiple layers can improve the classification performance: 75% with two-layers and 83% with three-layers model. Indian Society for Education and Environment & Informatics Publishing Limited 2018-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/62341/1/Real-Time%20Human%20Action%20Recognition.pdf Farooq, Adnan and Mohammad, Emad U Din and Ahmad Zarir, Abdullah and Ismail, Amelia Ritahani and Sulaiman, Suriani (2018) Real-time human action recognition using stacked sparse autoencoders. Indian Journal of Science and Technology, 11 (4). pp. 1-6. ISSN 0974-6846 E-ISSN 0974-5645 http://www.indjst.org/index.php/indjst/article/view/121090/83462 |
| spellingShingle | QA75 Electronic computers. Computer science Farooq, Adnan Mohammad, Emad U Din Ahmad Zarir, Abdullah Ismail, Amelia Ritahani Sulaiman, Suriani Real-time human action recognition using stacked sparse autoencoders |
| title | Real-time human action recognition using stacked sparse autoencoders |
| title_full | Real-time human action recognition using stacked sparse autoencoders |
| title_fullStr | Real-time human action recognition using stacked sparse autoencoders |
| title_full_unstemmed | Real-time human action recognition using stacked sparse autoencoders |
| title_short | Real-time human action recognition using stacked sparse autoencoders |
| title_sort | real-time human action recognition using stacked sparse autoencoders |
| topic | QA75 Electronic computers. Computer science |
| url | http://irep.iium.edu.my/62341/ http://irep.iium.edu.my/62341/ http://irep.iium.edu.my/62341/1/Real-Time%20Human%20Action%20Recognition.pdf |