Human body model for monocular body tracking
type: Dataset
description: Human body pose estimation or tracking using computer vision is a difficult task owing to the high dimensionality of the pose space. Learning based aproachs, especially methods using embeding spaces such as LLE [RS00] or GPLVM [Law03] can cope with this di culty but are restricted to cyclic motions [UFF06]. Other methods proceed in comparing a test image to a learning base. Among them, PSH [SVD03] is usefull to speed up the selection of a subset of nearest neighbours from large learning bases. However, even when pose regression is used to extrapolate new poses from the learned ones, a su cient coverage of the pose space is diffi cult to reach with learning-based approaches [TSDD06]. Other ways consist in using deterministic or stochastic algorithms. The former kind of methods often provide suboptimal solutions because they get stuck on local minima owing to monocular vision ambiguities [PF03]. Stochastic approachs are used to explore the posterior probability function but once again, the high dimensionality of the pose space, especially in the case of simulation-based methods using sampling, requires a huge number of samples to explore the main mode. An interesting solution consists in using a loose-limbed body model [SBR+04] where the likelihood of each limb is evaluated independently. In this manner the dimension of the explored space is reduced to the number of dof of a limb. In uence between limbs is estimated by belief propagation [KFL01] to provide consistent body poses. This last solution is adopted in this thesis in association with particle ltering to provide a discrete space where the beliefs are computed [BCMC06]. This method is prefered to a parametric modelling of beliefs using Gibbs sampler, a method derived from the PAMPAS algorithm [Isa03] involving heavy computational load. However, in addition to this solution, robust human body tracking, even in two dimensions [NB07b], requires to use several images cues. Thus, hypotheses likelihood is evaluated f…
identifier: NNT: 2007PA066640
issued: 2026-05-11T16:45:42.064061
language: fr
modified: 2026-05-11T16:45:42.064065
contact point: Organization
  fn: CCSD
keyword: data-unavailable
keyword: extraction-des-caracteristiques-multi-indices
keyword: filtre-a-particules
keyword: graphes-de-facteurs
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-mocomputer-science-csmodeling-and-simulation
keyword: propagation-des-croyances
keyword: soustraction-de-fond
keyword: suivi-monoculaire-du-corps-humain
keyword: theses
landing page: https://theses.hal.science/tel-00807950

Human body model for monocular body tracking
type: Distribution
format: HTML
issued: 2026-05-11T16:45:42.074089
modified: 2026-05-11T16:45:42.052813
access URL: https://theses.hal.science/tel-00807950

test_moissonnage_selune
type: Agent