9th Joint International Conference on Information Sciences (JCIS-06)

A Quick Self-Adaptive Background Updating Algorithm Based on Moving Region

Authors
Bo Qin 0, Chuangde Zhang, Zhenghua Fang, Wei Li
Corresponding Author
Bo Qin
0Computer Science Department of Ocean University of China
Available Online undefined NaN.
DOI
https://doi.org/10.2991/jcis.2006.21How to use a DOI?
Keywords
Motion detection; Background subtraction; Background updating; Label
Abstract
Background subtraction is a common way of real-time motion detection. Owing to lacking the right approach to update the background, it was confined in certain aspects. In this paper, we will present a quick self-adaptive background updating algorithm based on moving region, and use label method to de-noise the moving objects. The algorithm can reduce the calculation and accelerate the background subtraction. It also can update the background exactly along with the variance of illumination, and meet the real-time processing. While the label method can de-noise all the noises in the moving objects, the moving objects can be segmented from the background perfectly. The images in our experiment are obtained from the traffic scenes, and the experimental results show that this builds a good foundation for further shadow elimination and object recognition.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Publication Date
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ISBN
978-90-78677-01-7
DOI
https://doi.org/10.2991/jcis.2006.21How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Bo Qin
AU  - Chuangde Zhang
AU  - Zhenghua Fang
AU  - Wei Li
PY  - NaN/NaN
DA  - NaN/NaN
TI  - A Quick Self-Adaptive Background Updating Algorithm Based on Moving Region
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.21
DO  - https://doi.org/10.2991/jcis.2006.21
ID  - QinNaN/NaN
ER  -