Multi-view latent variable discriminative models for action recognition

  • Song Y
  • Morency L
  • Davis R
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Many human action recognition tasks involve data that can be
factorized into multiple views such as body postures and hand shapes. These
views often interact with each other over time, providing important cues to
understanding the action. We present multi-view latent variable discriminative
models that jointly learn both view-shared and view-specific sub-structures to
capture the interaction between views. Knowledge about the underlying structure
of the data is formulated as a multi-chain structured latent conditional model,
explicitly learning the interaction between multiple views using disjoint sets
of hidden variables in a discriminative manner. The chains are tied using a
predetermined topology that repeats over time. We present three topologies -
linked, coupled, and linked-coupled - that differ in the type of interaction
between views that they model. We evaluate our approach on both segmented and
unsegmented human action recognition tasks, using the ArmGesture, the NATOPS,
and the ArmGesture-Continuous data. Experimental results show that our approach
outperforms previous state-of-the-art action recognition models.

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