Coupled Visual and Kinematic Manifold Models for Tracking

In this paper, we consider modeling data lying on multiple continuous manifolds. In particular, we model the shape manifold of a person performing a motion observed from different viewpoints along a view circle at a fixed camera height. We introduce a model that ties together the body configuration...

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Bibliographic Details
Published in:International journal of computer vision 2010-03, Vol.87 (1-2), p.118-139
Main Authors: Lee, C.-S., Elgammal, A.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Cameras
Computer Imaging
Computer Science
Image Processing and Computer Vision
Kinematics
Manifolds
Mathematical models
Pattern Recognition
Pattern Recognition and Graphics
Representations
Studies
Three dimensional
Tracking
Vision
Visual
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Summary:In this paper, we consider modeling data lying on multiple continuous manifolds. In particular, we model the shape manifold of a person performing a motion observed from different viewpoints along a view circle at a fixed camera height. We introduce a model that ties together the body configuration (kinematics) manifold and visual (observations) manifold in a way that facilitates tracking the 3D configuration with continuous relative view variability. The model exploits the low-dimensionality nature of both the body configuration manifold and the view manifold, where each of them are represented separately. The resulting representation is used for tracking complex motions within a Bayesian framework, in which the model provides a low-dimensional state representation as well as a constrained dynamic model for both body configuration and view variations. Experimental results estimating the 3D body posture from a single camera are presented for the HUMANEVA dataset and other complex motion video sequences.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-009-0266-5