Real-time gesture recognition with minimal training requirements and on-line learning

Stjepan Rajko, Gang Qian, Todd Ingalls, Jodi James

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

34 Citations (Scopus)

Abstract

In this paper, we introduce the semantic network model (SNM), a generalization of the hidden Markov model (HMM) that uses factorization of state transition probabilities to reduce training requirements, increase the efficiency of gesture recognition and on-line learning, and allow more precision in gesture modeling. We demonstrate the advantages both formally and experimentally, using examples such as full-body multimodal gesture recognition via optical motion capture and a pressure sensitive floor, as well as mouse / pen gesture recognition. Our results show that our algorithm performs much better than the traditional approach in situations where training samples are limited and/or the precision of the gesture model is high.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - Oct 11 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

Fingerprint

Gesture recognition
Hidden Markov models
Factorization
Semantics

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Rajko, S., Qian, G., Ingalls, T., & James, J. (2007). Real-time gesture recognition with minimal training requirements and on-line learning. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270328] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383330

Real-time gesture recognition with minimal training requirements and on-line learning. / Rajko, Stjepan; Qian, Gang; Ingalls, Todd; James, Jodi.

2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. 4270328 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

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

Rajko, S, Qian, G, Ingalls, T & James, J 2007, Real-time gesture recognition with minimal training requirements and on-line learning. in 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07., 4270328, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07, Minneapolis, MN, United States, 6/17/07. https://doi.org/10.1109/CVPR.2007.383330
Rajko S, Qian G, Ingalls T, James J. Real-time gesture recognition with minimal training requirements and on-line learning. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. 4270328. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383330
Rajko, Stjepan ; Qian, Gang ; Ingalls, Todd ; James, Jodi. / Real-time gesture recognition with minimal training requirements and on-line learning. 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
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