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Proceedings Paper

Fast non-parametric background subtraction for infrared surveillance
Author(s): Shu-le Ge; Ting-fa Xu; Guo-qiang Ni
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Paper Abstract

Background subtraction is a method typically used to extract foreground objects in image sequences taken from static cameras by comparing each new frame to a background model, and it plays an important role in many vision application systems. In this paper, we introduce a non-parametric background subtraction method. Standard kernel density estimation method is very time consumptive, so it is modified by substituting the Gaussian kernel function with Epanechnikov kernel function and some optimizing techniques are adopted to improve its performance. As fluctuation is the intrinsic character of infrared image, we develop a bi-threshold updating method and a gradient based post-process method to reduce false positive error. Experiments show our method can extract intruding objects effectively and it outperforms threshold based method, especially when the intruder is not salient.

Paper Details

Date Published: 5 August 2009
PDF: 8 pages
Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 738348 (5 August 2009); doi: 10.1117/12.836059
Show Author Affiliations
Shu-le Ge, Beijing Institute of Technology (China)
Ting-fa Xu, Beijing Institute of Technology (China)
Guo-qiang Ni, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7383:
International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications
Jeffery Puschell; Hai-mei Gong; Yi Cai; Jin Lu; Jin-dong Fei, Editor(s)

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