Tracking Articulated Motion using a Mixture of Autoregressive Models - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2004

Tracking Articulated Motion using a Mixture of Autoregressive Models

Ankur Agarwal
  • Fonction : Auteur
  • PersonId : 844845
Bill Triggs

Résumé

We present a novel approach to modelling the non-linear and time-varying dynamics of human motion, using statistical methods to capture the characteristic motion patterns that exist in typical human activities. Our method is based on automatically clustering the body pose space into connected regions exhibiting similar dynamical characteristics, modelling the dynamics in each region as a Gaussian autoregressive process. Activities that would require large numbers of exemplars in example based methods are covered by comparatively few motion models. Different regions correspond roughly to different action-fragments and our class inference scheme allows for smooth transitions between these, thus making it useful for activity recognition tasks. The method is used to track activities including walking, running, etc., using a planar 2D body model. Its effectiveness is demonstrated by its success in tracking complicated motions like turns, without any key frames or 3D information.
Fichier principal
Vignette du fichier
Agarwal-eccv04.pdf (903.5 Ko) Télécharger le fichier
Vignette du fichier
1016c.png (69.95 Ko) Télécharger le fichier
runtest.mpeg (762.07 Ko) Télécharger le fichier
turncolor.mpeg (834.33 Ko) Télécharger le fichier
walk.mpeg (989.29 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Figure, Image
Format : Autre
Format : Autre
Format : Autre
Loading...

Dates et versions

inria-00548550 , version 1 (20-12-2010)

Identifiants

Citer

Ankur Agarwal, Bill Triggs. Tracking Articulated Motion using a Mixture of Autoregressive Models. European Conference on Computer Vision (ECCV '04), May 2004, Prague, Czech Republic. pp.54--65, ⟨10.1007/978-3-540-24672-5_5⟩. ⟨inria-00548550⟩
211 Consultations
589 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More