Aligning spatio-temporal signals on a special manifold

  • Authors:

  • Ruonan Li;Rama Chellappa

  • Affiliations:

  • Center for Automation Research, University of Maryland College Park, MD;Center for Automation Research, University of Maryland College Park, MD

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
  • Year:
  • 2010

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Abstract

We investigate the spatio-temporal alignment of videos or features/signals extracted from them. Specifically, we formally define an alignment manifold and formulate the alignment problem as an optimization procedure on this non-linear space by exploiting its intrinsic geometry. We focus our attention on semantically meaningful videos or signals, e.g., those describing or capturing human motion or activities, and propose a new formalism for temporal alignment accounting for executing rate variations among realizations of the same video event. By construction, we address this static and deterministic alignment task in a dynamic and stochastic manner: we regard the search for optimal alignment parameters as a recursive state estimation problem for a particular dynamic system evolving on the alignment manifold. Consequently, a Sequential Importance Sampling iteration on the alignment manifold is designed for effective and efficient alignment. We demonstrate the performance on several types of input data that arise in vision problems.