DocumentCode :
3139768
Title :
A Backmapping Approach for Graph-Based Object Tracking
Author :
Paixao, T.M. ; Graciano, Ana Beatriz V ; Cesar, Roberto M. ; Hirata, Ryuichi
Author_Institution :
Inst. of Math. & Stat., Univ. of Sao Paulo, Sao Paulo
fYear :
2008
fDate :
12-15 Oct. 2008
Firstpage :
45
Lastpage :
52
Abstract :
Model-based methods play a central role to solve different problems in computer vision. A particular important class of such methods rely on graph models where an object is decomposed into a number of parts, each one being represented by a graph vertex. A graph model-based tracking algorithm has been recently introduced in which a model is generated for a given frame (reference frame) and used to track a target object in the subsequent ones. Because the view of an object changes along the video sequence, the solution updated the model using affine transformations. This paper proposes a different approach and improves the previous one in several ways. Firstly, instead of updating the model, each analyzed frame is backmapped to the model space, thus providing more robustness to the method because model parameters do not have to be modified. A different method for model generation based on user traces has also been implemented and used. This model generation approach is much simpler and user-friendly. Finally, a graph-matching algorithm that has been recently proposed is used for object tracking. This new algorithm is more efficient and leads to better matching results. Experimental results using synthetic and real sequences from the CAVIAR project are shown and discussed.
Keywords :
affine transforms; computer vision; graph theory; image matching; image sequences; object detection; video signal processing; CAVIAR project; affine transformations; backmapping approach; computer vision; graph models; graph vertex; graph-based object tracking; graph-matching algorithm; model-based methods; video sequence; Computer graphics; Computer vision; Image processing; Image segmentation; Mathematical model; Mathematics; Pattern recognition; Robustness; Statistics; Target tracking; Attributed Relational Graph; Object Tracking; Structural Pattern Recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Graphics and Image Processing, 2008. SIBGRAPI '08. XXI Brazilian Symposium on
Conference_Location :
Campo Grande
ISSN :
1530-1834
Print_ISBN :
978-0-7695-3358-2
Type :
conf
DOI :
10.1109/SIBGRAPI.2008.32
Filename :
4654142
Link To Document :
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