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» Tracking Discontinuous Motion Using Bayesian Inference
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CVPR
1999
IEEE
15 years 11 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
ICCV
2003
IEEE
15 years 11 months ago
Filtering Using a Tree-Based Estimator
Within this paper a new framework for Bayesian tracking is presented, which approximates the posterior distribution at multiple resolutions. We propose a tree-based representation...
Bjoern Stenger, Arasanathan Thayananthan, Philip H...
CVPR
2008
IEEE
15 years 4 months ago
3D face tracking and expression inference from a 2D sequence using manifold learning
We propose a person-dependent, manifold-based approach for modeling and tracking rigid and nonrigid 3D facial deformations from a monocular video sequence. The rigid and nonrigid ...
Wei-Kai Liao, Gérard G. Medioni
ICCV
2001
IEEE
15 years 11 months ago
Human Tracking with Mixtures of Trees
Tree-structured probabilistic models admit simple, fast inference. However, they are not well suited to phenomena such as occlusion, where multiple components of an object may dis...
Sergey Ioffe, David A. Forsyth
ECCV
2000
Springer
15 years 11 months ago
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
A probabilistic method for tracking 3D articulated human figures in monocular image sequences is presented. Within a Bayesian framework, we define a generative model of image appea...
Hedvig Sidenbladh, Michael J. Black, David J. Flee...