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Recognizing Human Actions Using NWFE-Based Histogram Vectors

Abstract

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.

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Correspondence to Fu-Song Hsu.

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Lin, CH., Hsu, FS. & Lin, WY. Recognizing Human Actions Using NWFE-Based Histogram Vectors. EURASIP J. Adv. Signal Process. 2010, 453064 (2010). https://doi.org/10.1155/2010/453064

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Keywords

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