CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2009-04-27
Cited: 2
Clicked: 5884
Xuan-he WANG, Ji-lin LIU. Tracking multiple people under occlusion and across cameras using probabilistic models[J]. Journal of Zhejiang University Science A, 2009, 10(7): 985-996.
@article{title="Tracking multiple people under occlusion and across cameras using probabilistic models",
author="Xuan-he WANG, Ji-lin LIU",
journal="Journal of Zhejiang University Science A",
volume="10",
number="7",
pages="985-996",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820474"
}
%0 Journal Article
%T Tracking multiple people under occlusion and across cameras using probabilistic models
%A Xuan-he WANG
%A Ji-lin LIU
%J Journal of Zhejiang University SCIENCE A
%V 10
%N 7
%P 985-996
%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820474
TY - JOUR
T1 - Tracking multiple people under occlusion and across cameras using probabilistic models
A1 - Xuan-he WANG
A1 - Ji-lin LIU
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 7
SP - 985
EP - 996
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820474
Abstract: Tracking multiple people under occlusion and across cameras is a challenging question for discussion. Furthermore, the cameras in this study are used to extend the field of view, which are distinguished from the same field of view. Such correspondence between multiple cameras is a burgeoning research subject in the area of computer vision. This paper effectively solves the problems of tracking multiple people who pass from one camera to another and segmenting people under occlusion using probabilistic models. The probabilistic models are composed of blob model, motion model and color model, which make the most of the space, motion and color information. First, we present a color model that uses maximum likelihood estimation based on non-parametric kernel density estimation. Second, we introduce a blob model based on mean shift, which segments the body into many regions according to the color of each person in order to spatially localize the color features corresponding to the way people are dressed. Clothes can be any mixture of colors. Third, we bring forward a motion model based on statistical probability which indicates the movement position of the same person between two successive frames in a single camera. Finally, we effectively unify the three models into a general probabilistic model and attain a maximization likelihood probability image, which is used to segment the foreground region under occlusion and to match people across multiple cameras.
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