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

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.

Human action recognition using transfer learning with deep representations

“…The action recognition using handcrafted features descriptors such as extended SURF [22], HOG-3D [23], and some other shape and motion based features descriptors [24][25][26][27][28] have achieved remarkable performance for human action recognition. However, these approaches have several limitations: Handcrafted feature-based techniques require expert designed feature detectors, descriptors, and vocabulary building methods for feature extraction and representation.…”

A Novel 3D Gradient LBP Descriptor for Action Recognition

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