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ECCV
2002
Springer
14 years 8 months ago
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an ...
Hedvig Sidenbladh, Michael J. Black, Leonid Sigal
ICMCS
2008
IEEE
211views Multimedia» more  ICMCS 2008»
14 years 21 days ago
A real-time video surveillance system with human occlusion handling using nonlinear regression
This paper presents a real-time single-camera surveillance system, aiming at detecting and partly analyzing a group of people. A set of moving persons is segmented using a combina...
Jungong Han, Minwei Feng, Peter H. N. de With
CVPR
2010
IEEE
14 years 2 months ago
Dynamical Binary Latent Variable Models for 3D Human Pose Tracking
We introduce a new class of probabilistic latent variable model called the Implicit Mixture of Conditional Restricted Boltzmann Machines (imCRBM) for use in human pose tracking. K...
Graham Taylor, Leonid Sigal, David Fleet, Geoffrey...
HUMO
2007
Springer
14 years 13 days ago
Nonparametric Density Estimation with Adaptive, Anisotropic Kernels for Human Motion Tracking
In this paper, we suggest to model priors on human motion by means of nonparametric kernel densities. Kernel densities avoid assumptions on the shape of the underlying distribution...
Thomas Brox, Bodo Rosenhahn, Daniel Cremers, Hans-...
GI
2007
Springer
14 years 13 days ago
Background Modeling Using Adaptive Cluster Density Estimation for Automatic Human Detection
: Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background s...
Harish Bhaskar, Lyudmila Mihaylova, Simon Maskell