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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2011-01-01
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Series: | EURASIP Journal on Image and Video Processing |
Online Access: | http://jivp.eurasipjournals.com/content/2011/1/13 |
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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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1725550558278844416 |