CLC number: TP751.1
On-line Access: 2012-10-01
Received: 2012-03-15
Revision Accepted: 2012-08-24
Crosschecked: 2012-09-11
Cited: 1
Clicked: 7031
Yu Zhou, An-wen Shen, Jin-bang Xu. Non-interactive automatic video segmentation of moving targets[J]. Journal of Zhejiang University Science C, 2012, 13(10): 736-749.
@article{title="Non-interactive automatic video segmentation of moving targets",
author="Yu Zhou, An-wen Shen, Jin-bang Xu",
journal="Journal of Zhejiang University Science C",
volume="13",
number="10",
pages="736-749",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1200071"
}
%0 Journal Article
%T Non-interactive automatic video segmentation of moving targets
%A Yu Zhou
%A An-wen Shen
%A Jin-bang Xu
%J Journal of Zhejiang University SCIENCE C
%V 13
%N 10
%P 736-749
%@ 1869-1951
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1200071
TY - JOUR
T1 - Non-interactive automatic video segmentation of moving targets
A1 - Yu Zhou
A1 - An-wen Shen
A1 - Jin-bang Xu
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 10
SP - 736
EP - 749
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1200071
Abstract: Extracting moving targets from video accurately is of great significance in the field of intelligent transport. To some extent, it is related to video segmentation or matting. In this paper, we propose a non-interactive automatic segmentation method for extracting moving targets. First, the motion knowledge in video is detected with orthogonal Gaussian-Hermite moments and the Otsu algorithm, and the knowledge is treated as foreground seeds. Second, the background seeds are generated with distance transformation based on foreground seeds. Third, the foreground and background seeds are treated as extra constraints, and then a mask is generated using graph cuts methods or closed-form solutions. Comparison showed that the closed-form solution based on soft segmentation has a better performance and that the extra constraint has a larger impact on the result than other parameters. Experiments demonstrated that the proposed method can effectively extract moving targets from video in real time.
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