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CLC number: TP391.41
On-line Access:
Received: 2007-05-23
Revision Accepted: 2007-08-21
Crosschecked: 0000-00-00
Cited: 2
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GSM-MRF based classification approach for real-time moving object detection
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Xiang PAN, Yi-jun WU. GSM-MRF based classification approach for real-time moving object detection[J]. Journal of Zhejiang University Science A, 2008, 9(2): 250-255.
@article{title="GSM-MRF based classification approach for real-time moving object detection",
author="Xiang PAN, Yi-jun WU",
journal="Journal of Zhejiang University Science A",
volume="9",
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pages="250-255",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A071267"
}
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%T GSM-MRF based classification approach for real-time moving object detection
%A Xiang PAN
%A Yi-jun WU
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071267
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T1 - GSM-MRF based classification approach for real-time moving object detection
A1 - Xiang PAN
A1 - Yi-jun WU
J0 - Journal of Zhejiang University Science A
VL - 9
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SP - 250
EP - 255
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A071267
Abstract: Statistical and contextual information are typically used to detect moving regions in image sequences for a fixed camera. In this paper, we propose a fast and stable linear discriminant approach based on gaussian Single Model (GSM) and markov Random Field (MRF). The performance of GSM is analyzed first, and then two main improvements corresponding to the drawbacks of GSM are proposed: the latest filtered data based update scheme of the background model and the linear classification judgment rule based on spatial-temporal feature specified by MRF. Experimental results show that the proposed method runs more rapidly and accurately when compared with other methods.
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