ML Anthology

One Shot Learning Gesture Recognition from RGBD Images

Di Wu, Fan Zhu, Ling Shao
CVPRW 2012 pp. 7-12
doi:10.1109/CVPRW.2012.6239179 /cvprw/2012/wu2012cvprw-one/

Abstract

We present a system to classify the gesture from only one learning example. The inputs are duo-modality, i.e. RGB and depth sensor from Kinect. Our system performs morphological denoising on depth images and automatically segments the temporal boundaries. Features are extracted based on Extended-Motion-History-Image (Extended-MHI) and the Multi-view Spectral Embedding (MSE) algorithm is used to fuse duo modalities in a physically meaningful manner. Our approach achieves less than 0.3 in Levenshtein distance in CHALEARN Gesture Challenge validation batches [1].

CVPRW Semantic Scholar

Cite

Text

Wu et al. "One Shot Learning Gesture Recognition from RGBD Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012. doi:10.1109/CVPRW.2012.6239179

Markdown

[Wu et al. "One Shot Learning Gesture Recognition from RGBD Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2012.](https://mlanthology.org/cvprw/2012/wu2012cvprw-one/) doi:10.1109/CVPRW.2012.6239179

BibTeX

@inproceedings{wu2012cvprw-one,
  title     = {{One Shot Learning Gesture Recognition from RGBD Images}},
  author    = {Wu, Di and Zhu, Fan and Shao, Ling},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2012},
  pages     = {7-12},
  doi       = {10.1109/CVPRW.2012.6239179},
  url       = {https://mlanthology.org/cvprw/2012/wu2012cvprw-one/}
}