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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