AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios
<p/> <p>A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. The detection unit is composed of a Gaussian mixture model- (GMM-) based...
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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/2011/814285 |
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doaj-201de97b07924eeeb5af2655cea31f612020-11-25T02:30:07ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-51761687-52812011-01-0120111814285AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic ScenariosQuast KatharinaKaup André<p/> <p>A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. The detection unit is composed of a Gaussian mixture model- (GMM-) based moving foreground detection method followed by a method for determining reliable objects among the detected foreground regions using a projective transformation. Unlike the standard GMM detection the proposed detection method considers spatial and temporal dependencies as well as a limitation of the standard deviation leading to a faster update of the mixture model and to smoother binary masks. The binary masks are transformed in such a way that the object size can be used for a simple but fast classification. The core of the tracking unit, named GMM-SAMT, is a shape adaptive mean shift- (SAMT-) based tracking technique, which uses Gaussian mixture models to adapt the kernel to the object shape. GMM-SAMT returns not only the precise object position but also the current shape of the object. Thus, Auto GMM-SAMT achieves good tracking results even if the object is performing out-of-plane rotations.</p>http://jivp.eurasipjournals.com/content/2011/814285 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Quast Katharina Kaup André |
spellingShingle |
Quast Katharina Kaup André AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios EURASIP Journal on Image and Video Processing |
author_facet |
Quast Katharina Kaup André |
author_sort |
Quast Katharina |
title |
AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios |
title_short |
AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios |
title_full |
AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios |
title_fullStr |
AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios |
title_full_unstemmed |
AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios |
title_sort |
auto gmm-samt: an automatic object tracking system for video surveillance in traffic scenarios |
publisher |
SpringerOpen |
series |
EURASIP Journal on Image and Video Processing |
issn |
1687-5176 1687-5281 |
publishDate |
2011-01-01 |
description |
<p/> <p>A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. The detection unit is composed of a Gaussian mixture model- (GMM-) based moving foreground detection method followed by a method for determining reliable objects among the detected foreground regions using a projective transformation. Unlike the standard GMM detection the proposed detection method considers spatial and temporal dependencies as well as a limitation of the standard deviation leading to a faster update of the mixture model and to smoother binary masks. The binary masks are transformed in such a way that the object size can be used for a simple but fast classification. The core of the tracking unit, named GMM-SAMT, is a shape adaptive mean shift- (SAMT-) based tracking technique, which uses Gaussian mixture models to adapt the kernel to the object shape. GMM-SAMT returns not only the precise object position but also the current shape of the object. Thus, Auto GMM-SAMT achieves good tracking results even if the object is performing out-of-plane rotations.</p> |
url |
http://jivp.eurasipjournals.com/content/2011/814285 |
work_keys_str_mv |
AT quastkatharina autogmmsamtanautomaticobjecttrackingsystemforvideosurveillanceintrafficscenarios AT kaupandr233 autogmmsamtanautomaticobjecttrackingsystemforvideosurveillanceintrafficscenarios |
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