tailieunhanh - Báo cáo sinh học: " Research Article Recognizing Human Actions Using NWFE-Based Histogram Vectors"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Recognizing Human Actions Using NWFE-Based Histogram Vectors | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 453064 15 pages doi 2010 453064 Research Article Recognizing Human Actions Using NWFE-Based Histogram Vectors Cheng-Hsien Lin Fu-Song Hsu and Wei-Yang Lin Department of Computer Science and Information Engineering National Chung Cheng University Chiayi 621 Taiwan Correspondence should be addressed to Fu-Song Hsu hfs95p@ Received 15 December 2009 Revised 18 March 2010 Accepted 11 May 2010 Academic Editor ChangIck Kim Copyright 2010 Cheng-Hsien Lin et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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. 1. Introduction Recognizing human actions from video sequences is an important area of research in computer vision. This technology has many practical applications such as .