Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 901205 7 pages doi 2010 901205 Research Article Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns Ariel Amato Mikhail G. Mozerov F. Xavier Roca and Jordi Gonzalez Computer Vision Center CVC Universitat Autonoma de Barcelona Campus UAB Edifici O 08193 Bellaterra Spain Correspondence should be addressed to Mikhail G. Mozerov mozerov@ Received 1 December 2009 Accepted 21 June 2010 Academic Editor Yingzi Du Copyright 2010 Ariel Amato 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 paper describes an efficient background subtraction technique for detecting moving objects. The proposed approach is able to overcome difficulties like illumination changes and moving shadows. Our method introduces two discriminative features based on angular and modular patterns which are formed by similarity measurement between two sets of RGB color vectors one belonging to the background image and the other to the current image. We show how these patterns are used to improve foreground detection in the presence of moving shadows and in the case when there are strong similarities in color between background and foreground pixels. Experimental results over a collection of public and own datasets of real image sequences demonstrate that the proposed technique achieves a superior performance compared with state-of-the-art methods. Furthermore both the low computational and space complexities make the presented algorithm feasible for real-time applications. 1. Introduction Moving object detection is a crucial part of automatic video surveillance systems. One of the most common and effective approach to localize moving objects is background subtraction in which a model of the .
