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Cross-View Action Recognition from Temporal Self-similarities

  • Imran N. Junejo
  • Emilie Dexter
  • Ivan Laptev
  • Patrick Pérez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)

Abstract

This paper concerns recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such measures across views. Building upon this key observation we develop an action descriptor that captures the structure of temporal similarities and dissimilarities within an action sequence. Despite this descriptor not being strictly view-invariant, we provide intuition and experimental validation demonstrating the high stability of self-similarities under view changes. Self-similarity descriptors are also shown stable under action variations within a class as well as discriminative for action recognition. Interestingly, self-similarities computed from different image features possess similar properties and can be used in a complementary fashion. Our method is simple and requires neither structure recovery nor multi-view correspondence estimation. Instead, it relies on weak geometric properties and combines them with machine learning for efficient cross-view action recognition. The method is validated on three public datasets, it has similar or superior performance compared to related methods and it performs well even in extreme conditions such as when recognizing actions from top views while using side views for training only.

Keywords

Recognition Accuracy Action Recognition Human Action Recognition Gait Recognition Motion Capture Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Imran N. Junejo
    • 1
  • Emilie Dexter
    • 1
  • Ivan Laptev
    • 1
  • Patrick Pérez
    • 1
  1. 1.INRIA Rennes - Bretagne AtlantiqueRennes CedexFrance

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