Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2007: administrator
Metadata Last Update2020: administrator
Citation KeyGracianoCesaBloc:2007:GrObTr
TitleGraph-based Object Tracking Using Structural Pattern Recognition
FormatPrinted, On-line.
Access Date2021, Jan. 25
Number of Files1
Size2524 KiB
Context area
Author1 Graciano, Ana Beatriz Vicentim
2 Cesar-Jr, Roberto M.
3 Bloch, Isabelle
Affiliation1 Institute of Mathematics and Statistics - University of São Paulo
2 Institute of Mathematics and Statistics - University of São Paulo
3 Dept. TSI - ENST - Paris
EditorFalcão, Alexandre Xavier
Lopes, Hélio Côrtes Vieira
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
Conference LocationBelo Horizonte
DateOct. 7-10, 2007
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2007-07-31 18:46:15 :: -> administrator ::
2007-08-02 21:17:35 :: administrator -> ::
2008-07-17 14:09:43 :: -> administrator ::
2009-08-13 20:38:26 :: administrator -> banon ::
2010-08-28 20:02:28 :: banon -> administrator ::
2020-02-19 03:06:19 :: administrator -> :: 2007
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsobject tracking, recognition, part-based, graph, video.
AbstractThis paper proposes a model-based methodology for recognizing and tracking objects in digital image sequences. Objects are represented by attributed relational graphs (or ARGs), which carry both local and relational information about them. The recognition is performed by inexact graph matching, which consists in finding an approximate homomorphism between ARGs derived from an input video and a model image. Searching for a suitable homomorphism is achieved through a tree-search optimization algorithm and the minimization of a pre-defined cost function. Motion smoothness between successive frames is exploited to achieve the recognition over the whole sequence, with improved spatio-temporal coherence.
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Target Filegraciano-GraphBasedTracking.pdf
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