• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

DMCA

Coupled hidden Markov models for complex action recognition (1996)

Cached

  • Download as a PDF

Download Links

  • [whitechapel.media.mit.edu]
  • [www.cs.brown.edu]
  • [www.media.mit.edu]
  • [whitechapel.media.mit.edu]
  • [www.cs.cmu.edu]
  • [www.cs.cmu.edu]
  • [www.cs.cmu.edu]
  • [www.cs.cmu.edu]
  • [www.cs.cmu.edu]

  • Other Repositories/Bibliography

  • DBLP
  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Matthew Brand , Nuria Oliver , Alex Pentland
Citations:501 - 22 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Brand96coupledhidden,
    author = {Matthew Brand and Nuria Oliver and Alex Pentland},
    title = {Coupled hidden Markov models for complex action recognition},
    year = {1996}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However, the Markovian framework makes strong restrictive assumptions about the system generating the signal---that it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions. 1. Introduction Computer vision is turning to problems...

Keyphrases

hidden markov model    complex action recognition    conventional hmms    two-handed action    superior training speed    introduction computer vision    efficient way    model likelihood    clear bayesian semantics    vision task    strong restrictive assumption    dynamic behavior    limited state memory    low ceiling    perceptual computing    model performance    initial condition    single-process model    small number    markovian framework    dynamic time warping    single process    successful framework    training algorithm   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University