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  • Research Article
  • Open Access

Recognizing Human Actions Using NWFE-Based Histogram Vectors

EURASIP Journal on Advances in Signal Processing20102010:453064

  • Received: 15 December 2009
  • Accepted: 11 May 2010
  • Published:


This study presents a novel system for human action recognition. Two research issues, namely, motion representation and subspace learning, are addressed. In order to have a rich motion descriptor, we propose to combine the distance signal and the width feature so that a silhouette can be characterized in more detail. These two features provide complementary information and are integrated to yield a better discriminative power. The combined features are subsequently quantized into mid-level features using k-means clustering. In the mid-level feature space, we apply the Nonparametric Weighted Feature Extraction (NWFE) to construct a compact yet discriminative subspace model. Finally, we can simply train a Bayes classifier for recognizing human actions. We have conducted a series of experiments on two publicly available datasets to demonstrate the effectiveness of the proposed system. Compared with the existing approaches, our system has a significantly reduced complexity in classification stage while maintaining high accuracy.


  • Discriminative Power
  • Action Recognition
  • Distance Signal
  • Classification Stage
  • Combine Feature

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Authors’ Affiliations

Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, 621, Taiwan


© Cheng-Hsien Lin et al. 2010

This article is published under license to BioMed Central Ltd. 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.