Real-Time Object Detection Using Adaptive Background Model and Margined Sign Correlation
Summary :
Background subtraction is widely used in detecting moving objects; however, changing illumination conditions, color similarity, and real-time performance remain important problems. In this paper, we introduce a sequential method for adaptively estimating background components using Kalman filters, and a novel method for detecting objects using margined sign correlation (MSC). By applying MSC to our adaptive background model, the proposed system can perform object detection robustly and accurately. The proposed method is suitable for implementation on a graphics processing unit (GPU) and as such, the system realizes real-time performance efficiently. Experimental results demonstrate the performance of the proposed system.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.2 pp.325-335
- Publication Date
- 2011/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.325
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
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
Ayaka YAMAMOTO, Yoshio IWAI, Hiroshi ISHIGURO, "Real-Time Object Detection Using Adaptive Background Model and Margined Sign Correlation" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 2, pp. 325-335, February 2011, doi: 10.1587/transinf.E94.D.325.
Abstract: Background subtraction is widely used in detecting moving objects; however, changing illumination conditions, color similarity, and real-time performance remain important problems. In this paper, we introduce a sequential method for adaptively estimating background components using Kalman filters, and a novel method for detecting objects using margined sign correlation (MSC). By applying MSC to our adaptive background model, the proposed system can perform object detection robustly and accurately. The proposed method is suitable for implementation on a graphics processing unit (GPU) and as such, the system realizes real-time performance efficiently. Experimental results demonstrate the performance of the proposed system.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.325/_p
Copy
@ARTICLE{e94-d_2_325,
author={Ayaka YAMAMOTO, Yoshio IWAI, Hiroshi ISHIGURO, },
journal={IEICE TRANSACTIONS on Information},
title={Real-Time Object Detection Using Adaptive Background Model and Margined Sign Correlation},
year={2011},
volume={E94-D},
number={2},
pages={325-335},
abstract={Background subtraction is widely used in detecting moving objects; however, changing illumination conditions, color similarity, and real-time performance remain important problems. In this paper, we introduce a sequential method for adaptively estimating background components using Kalman filters, and a novel method for detecting objects using margined sign correlation (MSC). By applying MSC to our adaptive background model, the proposed system can perform object detection robustly and accurately. The proposed method is suitable for implementation on a graphics processing unit (GPU) and as such, the system realizes real-time performance efficiently. Experimental results demonstrate the performance of the proposed system.},
keywords={},
doi={10.1587/transinf.E94.D.325},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Real-Time Object Detection Using Adaptive Background Model and Margined Sign Correlation
T2 - IEICE TRANSACTIONS on Information
SP - 325
EP - 335
AU - Ayaka YAMAMOTO
AU - Yoshio IWAI
AU - Hiroshi ISHIGURO
PY - 2011
DO - 10.1587/transinf.E94.D.325
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2011
AB - Background subtraction is widely used in detecting moving objects; however, changing illumination conditions, color similarity, and real-time performance remain important problems. In this paper, we introduce a sequential method for adaptively estimating background components using Kalman filters, and a novel method for detecting objects using margined sign correlation (MSC). By applying MSC to our adaptive background model, the proposed system can perform object detection robustly and accurately. The proposed method is suitable for implementation on a graphics processing unit (GPU) and as such, the system realizes real-time performance efficiently. Experimental results demonstrate the performance of the proposed system.
ER -
English
