We present an analysis of the spatial covariance structure of an articulated motion prior in which joint angles have a known covariance structure. From this, a well-known, but often ignored, deficiency of the kinematic skeleton representation becomes clear: spatial variance not only depends on limb lengths, but also increases as the kinematic chains are traversed. We then present two similar Gaussian-like motion priors that are explicitly expressed spatially and as such avoids any variance coming from the representation. The resulting priors are both simple and easy to implement, yet they provide superior predictions. © 2010 Springer-Verlag.
CITATION STYLE
Hauberg, S., Sommer, S., & Pedersen, K. S. (2010). Gaussian-like spatial priors for articulated tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6311 LNCS, pp. 425–437). Springer Verlag. https://doi.org/10.1007/978-3-642-15549-9_31
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