ML Anthology

Real-Time Gesture Recognition with Minimal Training Requirements and On-Line Learning

Stjepan Rajko, Gang Qian, Todd Ingalls, Jodi James
CVPR 2007
doi:10.1109/CVPR.2007.383330 /cvpr/2007/rajko2007cvpr-real/

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.

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Cite

Text

Rajko et al. "Real-Time Gesture Recognition with Minimal Training Requirements and On-Line Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383330

Markdown

[Rajko et al. "Real-Time Gesture Recognition with Minimal Training Requirements and On-Line Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/rajko2007cvpr-real/) doi:10.1109/CVPR.2007.383330

BibTeX

@inproceedings{rajko2007cvpr-real,
  title     = {{Real-Time Gesture Recognition with Minimal Training Requirements and On-Line Learning}},
  author    = {Rajko, Stjepan and Qian, Gang and Ingalls, Todd and James, Jodi},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383330},
  url       = {https://mlanthology.org/cvpr/2007/rajko2007cvpr-real/}
}