Learning to track: conceptual manifold map for closed-form tracking

Our objective is to model the visual manifold of object appearance corresponding to geometric transformation. We learn a generative model for object appearance where the appearance of the object at each new frame is a function that maps from a conceptual representation of the geometric transformatio...

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 724 - 730 vol. 1
Main Author Elgammal, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Application software
Computer science
Computer vision
Human robot interaction
Layout
Lighting
Medical robotics
Search problems
Solid modeling
Tracking
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Summary:Our objective is to model the visual manifold of object appearance corresponding to geometric transformation. We learn a generative model for object appearance where the appearance of the object at each new frame is a function that maps from a conceptual representation of the geometric transformation space into the visual manifold. By learning such generative model we can infer the geometric transformation (track) directly from the tracked object appearance. As a result tracking can be achieved in a closed form and therefore can be done very efficiently.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.209