Tracking people on a torus

IEEE Transactions on Pattern Analysis and Machine Intelligence
Ahmed Elgammal, Chan-Su Lee

Abstract

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 a torus manifold. The approach we introduce here is based on the 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.

References

Aug 10, 2000·Neural Computation·J B Tenenbaum, W T Freeman
Dec 23, 2000·Science·J B TenenbaumJ C Langford
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Dec 18, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Jack M WangAaron Hertzmann

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Citations

Aug 21, 2013·IEEE Transactions on Cybernetics·Grégory RogezCarlos Orrite
Aug 20, 2014·IEEE Transactions on Cybernetics·Jesus Martinez-del-RinconDimitrios Makris
Oct 23, 2013·IEEE Transactions on Cybernetics·Michal LewandowskiJean-Christophe Nebel
Apr 12, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Mingyu FanHujun Bao
Apr 23, 2010·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Xin Zhang, Guoliang Fan
Dec 23, 2014·IEEE Transactions on Cybernetics·Meng Ding, Guolian Fan
Nov 27, 2019·Sensors·Mercedes Garcia-SalgueroFrancisco-Angel Moreno

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