tailieunhanh - Báo cáo hóa học: " Research Article Background Subtraction via Robust Dictionary Learning"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Background Subtraction via Robust Dictionary Learning | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 972961 12 pages doi 2011 972961 Research Article Background Subtraction via Robust Dictionary Learning Cong Zhao 1 Xiaogang Wang 1 2 and Wai-Kuen Cham1 department of Electrical Engineering The Chinese University of Hong Kong Hong Kong 2 Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Correspondence should be addressed to Cong Zhao czhao@ Received 14May2010 Revised29 September 2010 Accepted 18 January 2011 Academic Editor Luigi Di Stefano Copyright 2011 Cong Zhao 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. We propose a learning-based background subtraction approach based on the theory of sparse representation and dictionary learning. Our method makes the following two important assumptions 1 the background of a scene has a sparse linear representation over a learned dictionary 2 the foreground is sparse in the sense that majority pixels of the frame belong to the background. These two assumptions enable our method to handle both sudden and gradual background changes better than existing methods. As discussed in the paper the way of learning the dictionary is critical to the success of background modeling in our method. To build a correct background model when training samples are not foreground-free we propose a novel robust dictionary learning algorithm. It automatically prunes foreground pixels out as outliers at the learning stage. Experiments in both qualitative and quantitative comparisons with competing methods demonstrate the obtained robustness against background changes and better performance in foreground segmentation. 1. Introduction Segmenting foreground objects from a video sequence is a fundamental and critical step .