“Loose-Limbed People” Paradigm: Distributed Approach for Articulated Pose Estimation and Tracking
Speaker: Leonid Sigal , Brown UniversityContact:
Date: May 1 2006
Time: 2:00PM to 3:00PM
Location: Seminar Room D463 (Star)
Host: C. Mario Christoudias, Gerald Dalley, MIT CSAIL
C. Mario Christoudias, Gerald Dalley, 3-4278, 3-6095, firstname.lastname@example.org, email@example.comRelevant URL:
In the recent years we presented a number of methods for a fully automatic pose estimation and tracking of human bodies in 2D and 3D. Initialization and failure recovery in these methods are facilitated by the use of a loose-limbed body model in which limbs are connected via learned probabilistic constraints. The pose estimation and tracking can then be formulated as inference in a loopy graphical model and approximate belief propagation can be used to estimate the pose of the body. Each node in the graphical model represents the position and orientation of the limb, and the directed edges between nodes represent statistical dependencies between limbs. There are a number of significant advantages of this paradigm as compared to the more traditional methods for tracking human motion.
In this talk I will introduce the loose-limbed model paradigm and its application to 3D and 2D pose estimation and tracking. I will also show some preliminary results of a fully-automatic 3D hierarchical inference framework for pose estimation and tracking from a single view, where a 2D loose-limbed body model serves as an intermediate representation in the inference hierarchy.
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