NDLI: Adaptive Foreground Object Extraction for Real-Time Video Surveillance with Lighting Variations

Content Provider	IEEE Xplore Digital Library
Author	Hui-Chi Zeng Shang-Hong Lai
Copyright Year	2007
Description	Author affiliation: Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan (Hui-Chi Zeng; Shang-Hong Lai)
Abstract	In this paper we present an adaptive foreground object extraction algorithm for real-time video surveillance. The proposed algorithm improves the previous Gaussian mixture background models (GMMs) by applying a two-stage foreground/background classification procedure to remove the undesirable subtraction results due to shadow, automatic white balance, and sudden illumination change. The traditional background subtraction technique usually cannot work well for situations with lighting variations in the scene. In the proposed two-stage classification, an adaptive classifier is applied to the foreground pixels in a pixel-wise manner based on the normalized color and brightness gain information. Secondly, the remaining foreground candidate pixels are grouped into regions and the corresponding background regions are compared to check if they are foreground regions. Experimental results on some real surveillance video are shown to demonstrate the robustness of the proposed adaptive foreground extraction algorithm under a variety of different environments with lighting variations.
File Size	275086
File Format	PDF
ISBN	1424407273
ISSN	15206149
DOI	10.1109/ICASSP.2007.366129
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Video surveillance Subtraction techniques Robustness Gaussian distribution Probability density function Pixel Computer science Lighting Layout Brightness lighting variation Real-time surveillance background subtraction foreground extraction
Content Type	Text
Resource Type	Article

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