Moving object detection using keypoints reference model

<p>Abstract</p> <p>This article presents a new method for background subtraction (BGS) and object detection for a real-time video application using a combination of frame differencing and a scale-invariant feature detector. This method takes the benefits of background modelling and...

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Main Authors: Wan Zaki Wan Mimi Diyana, Hussain Aini, Hedayati Mohamed
Format: Article
Language:English
Published: SpringerOpen 2011-01-01
Series:EURASIP Journal on Image and Video Processing
Online Access:http://jivp.eurasipjournals.com/content/2011/1/13
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spelling doaj-8da26615f1ed469fa636b660a590d0c82020-11-24T23:28:07ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-51761687-52812011-01-012011113Moving object detection using keypoints reference modelWan Zaki Wan Mimi DiyanaHussain AiniHedayati Mohamed<p>Abstract</p> <p>This article presents a new method for background subtraction (BGS) and object detection for a real-time video application using a combination of frame differencing and a scale-invariant feature detector. This method takes the benefits of background modelling and the invariant feature detector to improve the accuracy in various environments. The proposed method consists of three main modules, namely, modelling, matching and subtraction modules. The comparison study of the proposed method with a popular Gaussian mixture model proved that the improvement in correct classification can be increased up to 98% with a reduction of false negative and true positive rates. Beside that the proposed method has shown great potential to overcome the drawback of the traditional BGS in handling challenges like shadow effect and lighting fluctuation.</p> http://jivp.eurasipjournals.com/content/2011/1/13
collection DOAJ
language English
format Article
sources DOAJ
author Wan Zaki Wan Mimi Diyana
Hussain Aini
Hedayati Mohamed
spellingShingle Wan Zaki Wan Mimi Diyana
Hussain Aini
Hedayati Mohamed
Moving object detection using keypoints reference model
EURASIP Journal on Image and Video Processing
author_facet Wan Zaki Wan Mimi Diyana
Hussain Aini
Hedayati Mohamed
author_sort Wan Zaki Wan Mimi Diyana
title Moving object detection using keypoints reference model
title_short Moving object detection using keypoints reference model
title_full Moving object detection using keypoints reference model
title_fullStr Moving object detection using keypoints reference model
title_full_unstemmed Moving object detection using keypoints reference model
title_sort moving object detection using keypoints reference model
publisher SpringerOpen
series EURASIP Journal on Image and Video Processing
issn 1687-5176
1687-5281
publishDate 2011-01-01
description <p>Abstract</p> <p>This article presents a new method for background subtraction (BGS) and object detection for a real-time video application using a combination of frame differencing and a scale-invariant feature detector. This method takes the benefits of background modelling and the invariant feature detector to improve the accuracy in various environments. The proposed method consists of three main modules, namely, modelling, matching and subtraction modules. The comparison study of the proposed method with a popular Gaussian mixture model proved that the improvement in correct classification can be increased up to 98% with a reduction of false negative and true positive rates. Beside that the proposed method has shown great potential to overcome the drawback of the traditional BGS in handling challenges like shadow effect and lighting fluctuation.</p>
url http://jivp.eurasipjournals.com/content/2011/1/13
work_keys_str_mv AT wanzakiwanmimidiyana movingobjectdetectionusingkeypointsreferencemodel
AT hussainaini movingobjectdetectionusingkeypointsreferencemodel
AT hedayatimohamed movingobjectdetectionusingkeypointsreferencemodel
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