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Fast video object tracking using affine invariant normalization

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dc.contributor.author Tzouveli, P en
dc.contributor.author Avrithis, Y en
dc.contributor.author Kollias, S en
dc.date.accessioned 2014-03-01T01:24:24Z
dc.date.available 2014-03-01T01:24:24Z
dc.date.issued 2006 en
dc.identifier.issn 15715736 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/17246
dc.subject Affine Transformation en
dc.subject Moving Object en
dc.subject Computer Vision and Image Processing en
dc.subject Video Object en
dc.title Fast video object tracking using affine invariant normalization en
heal.type journalArticle en
heal.identifier.primary 10.1007/0-387-34224-9_64 en
heal.identifier.secondary http://dx.doi.org/10.1007/0-387-34224-9_64 en
heal.publicationDate 2006 en
heal.abstract One of the most common problems in computer vision and image processing applications is the localization of object boundaries in a video frame and its tracking in the next frames. In this paper, a fully automatic method for fast tracking of video objects in a video sequence using affine invariant normalization is proposed. Initially, the detection of a video object is achieved using a GVF snake. Next, a vector of the affine parameters of each contour of the extracted video object in two successive frames is computed using affine-invariant normalization. Under the hypothesis that these contours are similar, the affine transformation between the two contours is computed in a very fast way. Using this transformation to predict the position of the contour in the next frame allows initialization of the GVF snake very close to the real position. Applying this technique to the following frames, a very fast tracking technique is achieved. Moreover, this technique can be applied on sequences with very fast moving objects where traditional trackers usually fail. Results on synthetic sequences are presented which illustrate the theoretical developments. © 2006 International Federation for Information Processing. en
heal.journalName IFIP International Federation for Information Processing en
dc.identifier.doi 10.1007/0-387-34224-9_64 en
dc.identifier.volume 204 en
dc.identifier.spage 549 en
dc.identifier.epage 556 en


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