A framework for motion recognition with applications to American sign language and gait recognition

C. Vogler, H. Sun, D. Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Human motion recognition has many important applications, such as improved human-computer interaction and surveillance. A big problem that plagues this research area is that human movements can be very complex. Managing this complexity is difficult. We turn to American sign language (ASL) recognition to identify general methods that reduce the complexity of human motion recognition. We present a framework for continuous 3D ASL recognition based on linguistic principles, especially the phonology of ASL. This framework is based on parallel hidden Markov models (HMMs), which are able to capture both the sequential and the simultaneous aspects of the language. Each HMM is based on a single phoneme of ASL. Because the phonemes are limited in number, as opposed to the virtually unlimited number of signs that can be composed from them, we expect this framework to scale well to larger applications. We then demonstrate the general applicability of this framework to other human motion recognition tasks by extending it to gait recognition.

Original languageEnglish (US)
Title of host publicationProceedings - Workshop on Human Motion, HUMO 2000
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages33-38
Number of pages6
ISBN (Electronic)0769509398, 9780769509396
DOIs
StatePublished - 2000
Externally publishedYes
EventWorkshop on Human Motion, HUMO 2000 - Austin, United States
Duration: Dec 7 2000Dec 8 2000

Publication series

NameProceedings - Workshop on Human Motion, HUMO 2000

Other

OtherWorkshop on Human Motion, HUMO 2000
Country/TerritoryUnited States
CityAustin
Period12/7/0012/8/00

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

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