Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns. (29th June 2010)

• Record Type: Journal Article
• Authors: Amato Amato, Ariel Ariel; Mozerov Mozerov, Mikhail G. Mikhail G.; Roca Roca, F. Xavier F. Xavier; Gonzàlez Gonzàlez, Jordi Jordi
• Other Names: Du Du Yingzi Yingzi Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-06-29
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/901205
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : 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.