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

fYear

1997

fDate

35597

Firstpage

10

Lastpage

17

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Nonrigid and Articulated Motion Workshop, 1997. Proceedings., IEEE

Conference_Location

San Juan

Print_ISBN

0-8186-8040-7

Type

conf

DOI

10.1109/NAMW.1997.609844

Filename

609844