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Multi-view human action recognition using wavelet data reduction and multi-class classification

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

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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.

Item Type:Article
Keyword:Multi-view; Human action recognition; Wavelet; Hierarchical classifier; SVM; Naïve Bayes
Faculty or Institute:Faculty of Computer Science and Information Technology
DOI Number:10.1016/j.procs.2015.08.540
ID Code:48080
Deposited By: Nabilah Mustapa
Deposited On:04 Aug 2016 16:47
Last Modified:04 Aug 2016 16:47

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