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CLC number: TP391.41

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Received: 2007-05-23

Revision Accepted: 2007-08-21

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.2 P.250~255

http://doi.org/10.1631/jzus.A071267


GSM-MRF based classification approach for real-time moving object detection


Author(s):  Xiang PAN, Yi-jun WU

Affiliation(s):  Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   panxiang@zju.edu.cn, wuyijun1984@hotmail.com

Key Words:  Moving object detection, Markov Random Field (MRF), Gaussian Single Model (GSM), Fisher Linear Discriminant Analysis (FLDA)


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.

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author="Xiang PAN, Yi-jun WU",
journal="Journal of Zhejiang University Science A",
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%DOI 10.1631/jzus.A071267

TY - JOUR
T1 - GSM-MRF based classification approach for real-time moving object detection
A1 - Xiang PAN
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J0 - Journal of Zhejiang University Science A
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SP - 250
EP - 255
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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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