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Appl. Sci. 2016, 6(10), 309; doi:10.3390/app6100309

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

1
School of Computing and Communications Infolab21, Lancaster University, Lancaster LA1 4WA, UK
2
Department of Computer Science, COMSATS Institute of Information Technology, Lahore 54000, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: David He
Received: 5 September 2016 / Revised: 7 October 2016 / Accepted: 13 October 2016 / Published: 21 October 2016
(This article belongs to the Special Issue Human Activity Recognition)
View Full-Text   |   Download PDF [2111 KB, uploaded 21 October 2016]   |  

Abstract

This paper presents a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. To achieve this, the human silhouette is divided into regions in a radial fashion with the interval of a certain degree, and then region-based geometrical and Hu-moments features are obtained from each radial bin to articulate the feature descriptor. A multiclass support vector machine classifier is used for action classification. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process. Our contribution is two-fold. Firstly, our approach achieves high recognition accuracy with simple silhouette-based representation. Secondly, the average testing time for our approach is 34 frames per second, which is much higher than the existing methods and shows its suitability for real-time applications. The extensive experiments on a well-known multiview IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset confirmed the superior performance of our method as compared to similar state-of-the-art methods. View Full-Text
Keywords: computer visions; human action recognition; view-invariant feature descriptor; classification; support vector machines computer visions; human action recognition; view-invariant feature descriptor; classification; support vector machines
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sargano, A.B.; Angelov, P.; Habib, Z. Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines. Appl. Sci. 2016, 6, 309.

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