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CLC number: TP391.41

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Received: 2005-01-25

Revision Accepted: 2005-06-22

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.100 P.47~52

10.1631/jzus.2005.AS0047


Video segmentation using Maximum Entropy Model


Author(s):  QIN Li-juan, ZHUANG Yue-ting, PAN Yun-he, WU Fei

Affiliation(s):  School of Computer Science, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   qinlijuan@hotmail.com

Key Words:  Layers segmentation, Maximum Entropy Model, Visual surveillance


QIN Li-juan, ZHUANG Yue-ting, PAN Yun-he, WU Fei. Video segmentation using Maximum Entropy Model[J]. Journal of Zhejiang University Science A, 2005, 6(100): 47~52.

@article{title="Video segmentation using Maximum Entropy Model",
author="QIN Li-juan, ZHUANG Yue-ting, PAN Yun-he, WU Fei",
journal="Journal of Zhejiang University Science A",
volume="6",
number="100",
pages="47~52",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.AS0047"
}

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%A ZHUANG Yue-ting
%A PAN Yun-he
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%D 2005
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%DOI 10.1631/jzus.2005.AS0047

TY - JOUR
T1 - Video segmentation using Maximum Entropy Model
A1 - QIN Li-juan
A1 - ZHUANG Yue-ting
A1 - PAN Yun-he
A1 - WU Fei
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 100
SP - 47
EP - 52
%@ 1673-565X
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PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.AS0047


Abstract: 
Detecting objects of interest from a video sequence is a fundamental and critical task in automated visual surveillance. Most current approaches only focus on discriminating moving objects by background subtraction whether or not the objects of interest can be moving or stationary. In this paper, we propose layers segmentation to detect both moving and stationary target objects from surveillance video. We extend the Maximum Entropy (ME) statistical model to segment layers with features, which are collected by constructing a codebook with a set of codewords for each pixel. We also indicate how the training models are used for the discrimination of target objects in surveillance video. Our experimental results are presented in terms of the success rate and the segmenting precision.

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

Reference

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[3] Chalidabhongse, T.H., Kim, K., Harwood, D., Davis, L., 2003. A Perturbation Method for Evaluation Background Subtraction Algorithms. Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

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[9] Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L., 2004. Background Modeling and Subtraction by Codebook Construction. IEEE International Conference on Image Processing.

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