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Green Image
Issue No. 03 - March (2009 vol. 31)
ISSN: 0162-8828
pp: 520-538
Ahmed Elgammal , Rutgers University, Piscataway
Chan-Su Lee , Rutgers University, Piscataway
We present a framework for monocular 3D kinematic pose tracking and viewpoint estimation of periodic and quasi-periodic human motions from an uncalibrated camera. The approach we introduce here is based on learning both the visual observation manifold and the kinematic manifold of the motion using a joint representation. We show that the visual manifold of the observed shape of a human performing a periodic motion, observed from different viewpoints, is topologically equivalent to {\em a torus manifold}. The approach we introduce here is based on {\em supervised} learning of both the visual and kinematic manifolds. Instead of learning an embedding of the manifold, we learn the geometric deformation between an ideal manifold (conceptual equivalent topological structure) and a twisted version of the manifold (the data). Experimental results show accurate estimation of the 3D body posture and the viewpoint from a single uncalibrated camera.
Motion, Shape, Video analysis

C. Lee and A. Elgammal, "Tracking People on a Torus," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 520-538, 2008.
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