Recognizing human actions using NWFE-based histogram vectors

Fu Song Hsu*, Cheng Hsien Lin, Wei Yang Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish
Article number453064
JournalEurasip Journal on Advances in Signal Processing
StatePublished - 2010


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