Exploiting spatio-temporal constraints for robust 2D pose tracking

  • Authors:

  • Grégory Rogez;Ignasi Rius;Jesús Martínez-Del-Rincón;Carlos Orrite

  • Affiliations:

  • Computer Vision Lab, I3A, University of Zaragoza, Spain;Computer Vision Center, UAB, Bellaterra, Spain;Computer Vision Lab, I3A, University of Zaragoza, Spain;Computer Vision Lab, I3A, University of Zaragoza, Spain

  • Venue:
  • Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
  • Year:
  • 2007

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Abstract

We present a Spatio-temporal 2D Models Framework (STMF) for 2D-Pose tracking. Space and time are discretized and a mixture of probabilistic "local models" is learnt associating 2D Shapes and 2D Stick Figures. Those spatio-temporal models generalize well for a particular viewpoint and state of the tracked action but some spatiotemporal discontinuities can appear along a sequence, as a direct consequence of the discretization. To overcome the problem, we propose to apply a Rao-Blackwellized Particle Filter (RBPF) in the 2D-Pose eigenspace, thus interpolating unseen data between view-based clusters. The fitness to the images of the predicted 2D-Poses is evaluated combining our STMF with spatio-temporal constraints. A robust, fast and smooth human motion tracker is obtained by tracking only the few most important dimensions of the state space and by refining deterministically with our STMF.