Recognizing human actions using NWFE-based histogram vectors

  • Authors:

  • Cheng-Hsien Lin;Fu-Song Hsu;Wei-Yang Lin

  • Affiliations:

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

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
  • Year:
  • 2010

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Abstract

This study presents a novel systemfor 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 whilemaintaining high accuracy.