5 August 2009 Fast non-parametric background subtraction for infrared surveillance
Author Affiliations +
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shu-le Ge, Shu-le Ge, Ting-fa Xu, Ting-fa Xu, Guo-qiang Ni, Guo-qiang Ni, } "Fast non-parametric background subtraction for infrared surveillance", 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; https://doi.org/10.1117/12.836059
PROCEEDINGS
8 PAGES


SHARE
RELATED CONTENT

Pfinder: real-time tracking of the human body
Proceedings of SPIE (January 03 1996)
Content-dependent frame-selective video compression
Proceedings of SPIE (November 17 2000)
Analysis of human motion in video imagery
Proceedings of SPIE (April 27 2010)
Statistical infrared image analysis
Proceedings of SPIE (September 29 1995)

Back to Top