Learning kinematic models for articulated objects
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning Articulated Structure and Motion
International Journal of Computer Vision
Affine warp propagation for fast simultaneous modelling and tracking of articulated objects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
A probabilistic framework for learning kinematic models of articulated objects
Journal of Artificial Intelligence Research
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Humans demonstrate a remarkable ability to parse complicated motion sequences into their constituent structures and motions. We investigate this problem, attempting to learn the structure of one or more articulated objects, given a time-series of two-dimensional feature positions. We model the observed sequence in terms of "stick figure" objects, under the assumption that the relative joint angles between sticks can change over time, but their lengths and connectivities are fixed. We formulate the problem in a single probabilistic model that includes multiple sub-components: associating the features with particular sticks, determining the proper number of sticks, and finding which sticks are physically joined. We test the algorithm on challenging datasets of 2D projections of optical human motion capture and feature trajectories from real videos.