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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


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.

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journal="Journal of Zhejiang University Science A",
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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
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.AS0047

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


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