Robust real-time background subtraction based on local neighborhood patterns

Authors:
Ariel Amato;Mikhail G. Mozerov;F. Xavier Roca;Jordi Gonzàlez
Affiliations:
Computer Vision Center, Universitat Autonoma de Barcelona, Bellaterra, Spain;Computer Vision Center, Universitat Autonoma de Barcelona, Bellaterra, Spain;Computer Vision Center, Universitat Autonoma de Barcelona, Bellaterra, Spain;Computer Vision Center, Universitat Autonoma de Barcelona, Bellaterra, Spain
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Year:
2010

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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.