Skip to Main content Skip to Navigation
Conference papers

Tracking Articulated Motion using a Mixture of Autoregressive Models

Ankur Agarwal 1 Bill Triggs 1 
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download
Contributor : THOTH Team Connect in order to contact the contributor
Submitted on : Monday, December 20, 2010 - 9:09:39 AM
Last modification on : Saturday, June 25, 2022 - 7:41:32 PM
Long-term archiving on: : Monday, March 21, 2011 - 3:16:14 AM




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⟩



Record views


Files downloads