We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic (PLSA) models to describe the mapping of image featu...
We present a novel approach to tracking 2D human motion in uncalibrated monocular videos. Human motion usually exhibits timevarying patterns, and we propose to use locally learnt ...
Abstract— For many tasks in populated environments, robots need to keep track of present and future motion states of people. Most approaches to people tracking make weak assumpti...
Matthias Luber, Johannes Andreas Stork, Gian Diego...
In this paper, we present a 3D registration algorithm based on simulated physical force/moment for articulated human motion tracking. Provided with sparsely reconstructed 3D human ...
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-...