Human pose tracking from monocular video by traversing an image motion mapped body pose manifold

Saurav Basu; Joshua Poulin; Scott T. Acton

doi:10.1117/12.848582

26 January 2010 Human pose tracking from monocular video by traversing an image motion mapped body pose manifold

Saurav Basu, Joshua Poulin, Scott T. Acton

Proceedings Volume 7533, Computational Imaging VIII; 753303 (2010) https://doi.org/10.1117/12.848582
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

Tracking human pose from monocular video sequences is a challenging problem due to the large number of independent parameters affecting image appearance and nonlinear relationships between generating parameters and the resultant images. Unlike the current practice of fitting interpolation functions to point correspondences between underlying pose parameters and image appearance, we exploit the relationship between pose parameters and image motion flow vectors in a physically meaningful way. Change in image appearance due to pose change is realized as navigating a low dimensional submanifold of the infinite dimensional Lie group of diffeomorphisms of the two dimensional sphere S². For small changes in pose, image motion flow vectors lie on the tangent space of the submanifold. Any observed image motion flow vector field is decomposed into the basis motion vector flow fields on the tangent space and combination weights are used to update corresponding pose changes in the different dimensions of the pose parameter space. Image motion flow vectors are largely invariant to style changes in experiments with synthetic and real data where the subjects exhibit variation in appearance and clothing. The experiments demonstrate the robustness of our method (within ±4° of ground truth) to style variance.

Citation Download Citation

Saurav Basu, Joshua Poulin, and Scott T. Acton "Human pose tracking from monocular video by traversing an image motion mapped body pose manifold", Proc. SPIE 7533, Computational Imaging VIII, 753303 (26 January 2010); https://doi.org/10.1117/12.848582

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KEYWORDS

Beam propagation method

Motion estimation

Cameras

Video

3D modeling

Kinematics

Optical spheres

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