Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines
AbstractThis 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
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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.
Sargano AB, Angelov P, Habib Z. Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines. Applied Sciences. 2016; 6(10):309.Chicago/Turabian Style
Sargano, Allah B.; Angelov, Plamen; Habib, Zulfiqar. 2016. "Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines." Appl. Sci. 6, no. 10: 309.
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