Tracking Articulated Motion using a Mixture of Autoregressive Models

Ankur Agarwal 1 Bill Triggs 1
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, 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.
Type de document :
Communication dans un congrès
Tomás Pajdla and Jiri Matas. European Conference on Computer Vision (ECCV '04), May 2004, Prague, Czech Republic. Springer-Verlag, III, pp.54--65, 2004, Lecture Notes in Computer Science (LNCS). 〈http://springerlink.metapress.com/content/mfknl5j1ex3ky18r/〉. 〈10.1007/978-3-540-24672-5_5〉
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Ankur Agarwal, Bill Triggs. Tracking Articulated Motion using a Mixture of Autoregressive Models. Tomás Pajdla and Jiri Matas. European Conference on Computer Vision (ECCV '04), May 2004, Prague, Czech Republic. Springer-Verlag, III, pp.54--65, 2004, Lecture Notes in Computer Science (LNCS). 〈http://springerlink.metapress.com/content/mfknl5j1ex3ky18r/〉. 〈10.1007/978-3-540-24672-5_5〉. 〈inria-00548550〉

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