ML Anthology

Hand Gesture Recognition with 3D Convolutional Neural Networks

Pavlo Molchanov, Shalini Gupta, Kihwan Kim, Jan Kautz
CVPRW 2015 pp. 1-7
doi:10.1109/CVPRW.2015.7301342 /cvprw/2015/molchanov2015cvprw-hand/

Abstract

Touchless hand gesture recognition systems are becoming important in automotive user interfaces as they improve safety and comfort. Various computer vision algorithms have employed color and depth cameras for hand gesture recognition, but robust classification of gestures from different subjects performed under widely varying lighting conditions is still challenging. We propose an algorithm for drivers' hand gesture recognition from challenging depth and intensity data using 3D convolutional neural networks. Our solution combines information from multiple spatial scales for the final prediction. It also employs spatio-temporal data augmentation for more effective training and to reduce potential overfitting. Our method achieves a correct classification rate of 77.5% on the VIVA challenge dataset.

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Cite

Text

Molchanov et al. "Hand Gesture Recognition with 3D Convolutional Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301342

Markdown

[Molchanov et al. "Hand Gesture Recognition with 3D Convolutional Neural Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/molchanov2015cvprw-hand/) doi:10.1109/CVPRW.2015.7301342

BibTeX

@inproceedings{molchanov2015cvprw-hand,
  title     = {{Hand Gesture Recognition with 3D Convolutional Neural Networks}},
  author    = {Molchanov, Pavlo and Gupta, Shalini and Kim, Kihwan and Kautz, Jan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2015},
  pages     = {1-7},
  doi       = {10.1109/CVPRW.2015.7301342},
  url       = {https://mlanthology.org/cvprw/2015/molchanov2015cvprw-hand/}
}