Title :
Estimation of articulated motion using kinematically constrained mixture densities
Author :
Hunter, E.A. ; Kelly, P.H. ; Jain, R.C.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
Abstract :
We address the problem of articulated posture estimation in its general form. Namely, the recovery of full 3D articulated posture parameters from an uncontrolled scene. Stochastic modeling of low-level segmented image data is unified with models of object kinematic structure through a constrained mixture of observation processes. A modified expectation-maximization algorithm is proposed for this purpose. Early experiments qualitatively demonstrate the efficacy of our approach, and provide a context for integration for more sophisticated image cues
Keywords :
image segmentation; image sequences; kinematics; motion estimation; parameter estimation; stochastic processes; 3D articulated posture parameter recovery; articulated motion estimation; articulated posture estimation; expectation-maximization algorithm; image cues; image sequences; kinematically constrained mixture densities; low-level segmented image data; object kinematic structure; observation processes; stochastic modeling; uncontrolled scene; Expectation-maximization algorithms; Humans; Image segmentation; Image sequences; Kinematics; Laboratories; Layout; Motion estimation; State estimation; Stochastic processes;
Conference_Titel :
Nonrigid and Articulated Motion Workshop, 1997. Proceedings., IEEE
Conference_Location :
San Juan
Print_ISBN :
0-8186-8040-7
DOI :
10.1109/NAMW.1997.609844
