Recognizing Human Actions Using NWFE-Based Histogram Vectors. (8th June 2010)

• Record Type: Journal Article
• Authors: Lin Lin, Cheng-Hsien Cheng-Hsien; Hsu Hsu, Fu-Song Fu-Song; Lin Lin, Wei-Yang Wei-Yang
• Other Names: Kim Kim ChangIck ChangIck Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-06-08
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/453064
• Languages: English
• ISSNs: 1687-6172

Abstract

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.