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Published Articles >> Table of Contents >> Abstract
IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2
pp. 8-13
Background Segmentation Using Spatial-Temporal Multi-Resolution MRF
Yue Zhou, University of Illinois at Urbana- Champaign
Wei Xu, NEC Laboratories America, Inc.
Hai Tao, University of California, Santa Cruz
Yihong Gong, NEC Laboratories America, Inc.
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ACVMOT.2005.32
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| Abstract |
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Robust and accurate background segmentation is crucial for
surveillance applications and is a key element in visual
tracking, layer-based compression, and silhouette-based 3D
reconstruction. In this paper, we present a novel spatial-temporal
model that describes the appearance and dynamics
of background scenes at multiple resolutions. We propose a
time-dependent Markov Random Field (MRF) to represent
the state of foreground and background at each pixel in the
spatial-temporal pyramid. Pixels are linked spatially and
temporally across frames. The probability of adding/deleting
a foreground object is calculated by online learning
algorithm and is used as prior information in computing
foreground label. We use Gibbs Sampling to solve the MRF
in a Maximum A Posterior (MAP) framework. Experimental
results show that this real-time algorithm is able to segment
the foreground object accurately from videos and more
resilient to distractions such as imaging noise, illumination
changes, camera shakes, and random motion in the scene.
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 Additional Information
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Citation:
Yue Zhou, Wei Xu, Hai Tao, Yihong Gong,
"Background Segmentation Using Spatial-Temporal Multi-Resolution MRF,"
wacv-motion,
pp. 8-13,
IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2,
2005
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