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


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


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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

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





Author: Allah Bux Sargano 1,2,* , Plamen Angelov 1 and Zulfiqar Habib 2

Source: http://mdpi.com/



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