Foreground-Adaptive Motion Detection in Broad Surveillance Environments
Summary :
In this letter, we propose a novel motion detection method in order to accurately perform the detection of moving objects in the automatic video surveillance system. Based on the proposed Background Generation Mechanism, the presence of either moving object or background information is firstly checked in order to supply the selective updating of the high-quality adaptive background model, which facilitates the further motion detection using the Laplacian distribution model. The overall results of the detection accuracy will be demonstrated that our proposed method attains a substantially higher degree of efficacy, outperforming the state-of-the-art method by average Similarity accuracy rates of up to 56.64%, 27.78%, 50.04%, 43.33%, and 44.09%, respectively.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E93-A No.11 pp.2096-2097
- Publication Date
- 2010/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E93.A.2096
- Type of Manuscript
- Special Section LETTER (Special Section on Information Theory and Its Applications)
- Category
- Image Processing
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Fan-Chieh CHENG, Shih-Chia HUANG, Shanq-Jang RUAN, "Foreground-Adaptive Motion Detection in Broad Surveillance Environments" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 11, pp. 2096-2097, November 2010, doi: 10.1587/transfun.E93.A.2096.
Abstract: In this letter, we propose a novel motion detection method in order to accurately perform the detection of moving objects in the automatic video surveillance system. Based on the proposed Background Generation Mechanism, the presence of either moving object or background information is firstly checked in order to supply the selective updating of the high-quality adaptive background model, which facilitates the further motion detection using the Laplacian distribution model. The overall results of the detection accuracy will be demonstrated that our proposed method attains a substantially higher degree of efficacy, outperforming the state-of-the-art method by average Similarity accuracy rates of up to 56.64%, 27.78%, 50.04%, 43.33%, and 44.09%, respectively.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.2096/_p
Copy
@ARTICLE{e93-a_11_2096,
author={Fan-Chieh CHENG, Shih-Chia HUANG, Shanq-Jang RUAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Foreground-Adaptive Motion Detection in Broad Surveillance Environments},
year={2010},
volume={E93-A},
number={11},
pages={2096-2097},
abstract={In this letter, we propose a novel motion detection method in order to accurately perform the detection of moving objects in the automatic video surveillance system. Based on the proposed Background Generation Mechanism, the presence of either moving object or background information is firstly checked in order to supply the selective updating of the high-quality adaptive background model, which facilitates the further motion detection using the Laplacian distribution model. The overall results of the detection accuracy will be demonstrated that our proposed method attains a substantially higher degree of efficacy, outperforming the state-of-the-art method by average Similarity accuracy rates of up to 56.64%, 27.78%, 50.04%, 43.33%, and 44.09%, respectively.},
keywords={},
doi={10.1587/transfun.E93.A.2096},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Foreground-Adaptive Motion Detection in Broad Surveillance Environments
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2096
EP - 2097
AU - Fan-Chieh CHENG
AU - Shih-Chia HUANG
AU - Shanq-Jang RUAN
PY - 2010
DO - 10.1587/transfun.E93.A.2096
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2010
AB - In this letter, we propose a novel motion detection method in order to accurately perform the detection of moving objects in the automatic video surveillance system. Based on the proposed Background Generation Mechanism, the presence of either moving object or background information is firstly checked in order to supply the selective updating of the high-quality adaptive background model, which facilitates the further motion detection using the Laplacian distribution model. The overall results of the detection accuracy will be demonstrated that our proposed method attains a substantially higher degree of efficacy, outperforming the state-of-the-art method by average Similarity accuracy rates of up to 56.64%, 27.78%, 50.04%, 43.33%, and 44.09%, respectively.
ER -
English
