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Viewpoint Manifolds for Action Recognition

Abstract

Action recognition from video is a problem that has many important applications to human motion analysis. In real-world settings, the viewpoint of the camera cannot always be fixed relative to the subject, so view-invariant action recognition methods are needed. Previous view-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit) that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available dataset previously only achieved using multiple simultaneous views.

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Correspondence to Richard Souvenir.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Souvenir, R., Parrigan, K. Viewpoint Manifolds for Action Recognition. J Image Video Proc 2009, 738702 (2009). https://doi.org/10.1155/2009/738702

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Keywords

  • Manifold
  • Recognition Rate
  • Action Recognition
  • Human Motion
  • Recognition Method