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

Applied Sciences

Zulfiqar Habib¹  Plamen Angelov²  Allah Bux Sargano² 

[1] Department of Computer Science, COMSATS Institute of Information Technology, Lahore 54000, Pakistan;School of Computing and Communications Infolab21, Lancaster University, Lancaster LA1 4WA, UK;

关键词: computer visions;    human action recognition;    view-invariant feature descriptor;    classification;    support vector machines;   

DOI  :  10.3390/app6100309

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown