Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation

<p>Abstract</p> <p>The paper suggests a contour-based algorithm for tracking moving objects in video. The inputs are segmented moving objects. Each segmented frame is transformed into region adjacency graphs (RAGs). The object's contour is divided into subcurves. Contour'...

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Main Authors: Averbuch A, Navon E, Miller O
Format: Article
Language:English
Published: SpringerOpen 2008-01-01
Series:EURASIP Journal on Image and Video Processing
Online Access:http://jivp.eurasipjournals.com/content/2008/328052
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spelling doaj-ace0671f8f624f5ba12f2992aacfb32d2020-11-24T20:53:39ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-51761687-52812008-01-0120081328052Tracking of Moving Objects in Video Through Invariant Features in Their Graph RepresentationAverbuch ANavon EMiller O<p>Abstract</p> <p>The paper suggests a contour-based algorithm for tracking moving objects in video. The inputs are segmented moving objects. Each segmented frame is transformed into region adjacency graphs (RAGs). The object's contour is divided into subcurves. Contour's junctions are derived. These junctions are the unique &#226;&#8364;&#339;signature&#226;&#8364;&#65533; of the tracked object. Junctions from two consecutive frames are matched. The junctions' motion is estimated using RAG edges in consecutive frames. Each pair of matched junctions may be connected by several paths (edges) that become candidates that represent a tracked contour. These paths are obtained by the <inline-formula> <graphic file="1687-5281-2008-328052-i1.gif"/></inline-formula>-shortest paths algorithm between two nodes. The RAG is transformed into a weighted directed graph. The final tracked contour construction is derived by a match between edges (subcurves) and candidate paths sets. The RAG constructs the tracked contour that enables an accurate and unique moving object representation. The algorithm tracks multiple objects, partially covered (occluded) objects, compounded object of merge/split such as players in a soccer game and tracking in a crowded area for surveillance applications. We assume that features of topologic signature of the tracked object stay invariant in two consecutive frames. The algorithm's complexity depends on RAG's edges and not on the image's size.</p>http://jivp.eurasipjournals.com/content/2008/328052
collection DOAJ
language English
format Article
sources DOAJ
author Averbuch A
Navon E
Miller O
spellingShingle Averbuch A
Navon E
Miller O
Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation
EURASIP Journal on Image and Video Processing
author_facet Averbuch A
Navon E
Miller O
author_sort Averbuch A
title Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation
title_short Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation
title_full Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation
title_fullStr Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation
title_full_unstemmed Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation
title_sort tracking of moving objects in video through invariant features in their graph representation
publisher SpringerOpen
series EURASIP Journal on Image and Video Processing
issn 1687-5176
1687-5281
publishDate 2008-01-01
description <p>Abstract</p> <p>The paper suggests a contour-based algorithm for tracking moving objects in video. The inputs are segmented moving objects. Each segmented frame is transformed into region adjacency graphs (RAGs). The object's contour is divided into subcurves. Contour's junctions are derived. These junctions are the unique &#226;&#8364;&#339;signature&#226;&#8364;&#65533; of the tracked object. Junctions from two consecutive frames are matched. The junctions' motion is estimated using RAG edges in consecutive frames. Each pair of matched junctions may be connected by several paths (edges) that become candidates that represent a tracked contour. These paths are obtained by the <inline-formula> <graphic file="1687-5281-2008-328052-i1.gif"/></inline-formula>-shortest paths algorithm between two nodes. The RAG is transformed into a weighted directed graph. The final tracked contour construction is derived by a match between edges (subcurves) and candidate paths sets. The RAG constructs the tracked contour that enables an accurate and unique moving object representation. The algorithm tracks multiple objects, partially covered (occluded) objects, compounded object of merge/split such as players in a soccer game and tracking in a crowded area for surveillance applications. We assume that features of topologic signature of the tracked object stay invariant in two consecutive frames. The algorithm's complexity depends on RAG's edges and not on the image's size.</p>
url http://jivp.eurasipjournals.com/content/2008/328052
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AT navone trackingofmovingobjectsinvideothroughinvariantfeaturesintheirgraphrepresentation
AT millero trackingofmovingobjectsinvideothroughinvariantfeaturesintheirgraphrepresentation
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