Multi-view human action recognition using wavelet data reduction and multi-class classification
Human action recognition from video has several potential to apply in different real-life applications, but the most cases in this field suffer from the variation in viewpoint. Most of published methods in this area are considered the performance of each single camera, therefore the change in the vi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/48080/ http://psasir.upm.edu.my/id/eprint/48080/1/48080.pdf |
| _version_ | 1848850983368523776 |
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| author | Aryanfar, Alihossein Yaakob, Razali Abdul Halin, Alfian Sulaiman, Md. Nasir Kasmiran, Khairul Azhar Mohammadpour, Leila |
| author_facet | Aryanfar, Alihossein Yaakob, Razali Abdul Halin, Alfian Sulaiman, Md. Nasir Kasmiran, Khairul Azhar Mohammadpour, Leila |
| author_sort | Aryanfar, Alihossein |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Human action recognition from video has several potential to apply in different real-life applications, but the most cases in this field suffer from the variation in viewpoint. Most of published methods in this area are considered the performance of each single camera, therefore the change in the viewpoints significantly decrease the recognition rate. In this paper, multiple views are considered together and a method has proposed to recognize human action depicted in multi-view image sequences. In the first step, the border of the human body's silhouette is extracted and distance signal is calculated. In the next step, the wavelet transform is applied to extract coefficients of single-view features, and then the extracted features are combined to compose multi-view features. Finally a hierarchical classifier using support vector machine and Naïve Bayes classifiers is implemented to classify the actions. The average of overall action recognition accuracy for 12 actions using 5 different angles of views on the IXMAS dataset is 88.22. The results of experiments on the popular multi-view dataset have shown the proposed method achieves high and state-of-the-art success rates. In other word, combination of single-view extracted features from the wavelet approximation coefficients and composing the multi-view features can be used as the multi-view features. Further, the hierarchical classifier can be applied to recognize actions in multi-view human action recognition area. |
| first_indexed | 2025-11-15T10:14:57Z |
| format | Article |
| id | upm-48080 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:14:57Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-480802016-08-04T08:47:52Z http://psasir.upm.edu.my/id/eprint/48080/ Multi-view human action recognition using wavelet data reduction and multi-class classification Aryanfar, Alihossein Yaakob, Razali Abdul Halin, Alfian Sulaiman, Md. Nasir Kasmiran, Khairul Azhar Mohammadpour, Leila Human action recognition from video has several potential to apply in different real-life applications, but the most cases in this field suffer from the variation in viewpoint. Most of published methods in this area are considered the performance of each single camera, therefore the change in the viewpoints significantly decrease the recognition rate. In this paper, multiple views are considered together and a method has proposed to recognize human action depicted in multi-view image sequences. In the first step, the border of the human body's silhouette is extracted and distance signal is calculated. In the next step, the wavelet transform is applied to extract coefficients of single-view features, and then the extracted features are combined to compose multi-view features. Finally a hierarchical classifier using support vector machine and Naïve Bayes classifiers is implemented to classify the actions. The average of overall action recognition accuracy for 12 actions using 5 different angles of views on the IXMAS dataset is 88.22. The results of experiments on the popular multi-view dataset have shown the proposed method achieves high and state-of-the-art success rates. In other word, combination of single-view extracted features from the wavelet approximation coefficients and composing the multi-view features can be used as the multi-view features. Further, the hierarchical classifier can be applied to recognize actions in multi-view human action recognition area. Elsevier 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/48080/1/48080.pdf Aryanfar, Alihossein and Yaakob, Razali and Abdul Halin, Alfian and Sulaiman, Md. Nasir and Kasmiran, Khairul Azhar and Mohammadpour, Leila (2015) Multi-view human action recognition using wavelet data reduction and multi-class classification. Procedia Computer Science, 62. pp. 585-592. ISSN 1877-0509 http://www.sciencedirect.com/science/article/pii/S1877050915026757 10.1016/j.procs.2015.08.540 |
| spellingShingle | Aryanfar, Alihossein Yaakob, Razali Abdul Halin, Alfian Sulaiman, Md. Nasir Kasmiran, Khairul Azhar Mohammadpour, Leila Multi-view human action recognition using wavelet data reduction and multi-class classification |
| title | Multi-view human action recognition using wavelet data reduction and multi-class classification |
| title_full | Multi-view human action recognition using wavelet data reduction and multi-class classification |
| title_fullStr | Multi-view human action recognition using wavelet data reduction and multi-class classification |
| title_full_unstemmed | Multi-view human action recognition using wavelet data reduction and multi-class classification |
| title_short | Multi-view human action recognition using wavelet data reduction and multi-class classification |
| title_sort | multi-view human action recognition using wavelet data reduction and multi-class classification |
| url | http://psasir.upm.edu.my/id/eprint/48080/ http://psasir.upm.edu.my/id/eprint/48080/ http://psasir.upm.edu.my/id/eprint/48080/ http://psasir.upm.edu.my/id/eprint/48080/1/48080.pdf |