RITnet: Real-Time Semantic Segmentation of the Eye for Gaze Tracking

Aayush K. Chaudhary, Rakshit Sunil Kothari, Manoj Acharya, Shusil Dangi, Nitinraj Nair, Reynold Bailey, Christopher Kanan, Gabriel J. Diaz, Jeff B. Pelz

ICCVW 2019 pp. 3698-3702

doi:10.1109/ICCVW.2019.00568 /iccvw/2019/chaudhary2019iccvw-ritnet/

Abstract

Accurate eye segmentation can improve eye-gaze estimation and support interactive computing based on visual attention; however, existing eye segmentation methods suffer from issues such as person-dependent accuracy, lack of robustness, and an inability to be run in real-time. Here, we present the RITnet model, which is a deep neural network that combines U-Net and DenseNet. RITnet is under 1 MB and achieves 95.3% accuracy on the 2019 OpenEDS Semantic Segmentation challenge. Using a GeForce GTX 1080 Ti, RITnet tracks at > 300Hz, enabling real-time gaze tracking applications. Pre-trained models and source code are available

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Cite

Text

Chaudhary et al. "RITnet: Real-Time Semantic Segmentation of the Eye for Gaze Tracking." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00568

Markdown

[Chaudhary et al. "RITnet: Real-Time Semantic Segmentation of the Eye for Gaze Tracking." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/chaudhary2019iccvw-ritnet/) doi:10.1109/ICCVW.2019.00568

BibTeX

@inproceedings{chaudhary2019iccvw-ritnet,
  title     = {{RITnet: Real-Time Semantic Segmentation of the Eye for Gaze Tracking}},
  author    = {Chaudhary, Aayush K. and Kothari, Rakshit Sunil and Acharya, Manoj and Dangi, Shusil and Nair, Nitinraj and Bailey, Reynold and Kanan, Christopher and Diaz, Gabriel J. and Pelz, Jeff B.},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2019},
  pages     = {3698-3702},
  doi       = {10.1109/ICCVW.2019.00568},
  url       = {https://mlanthology.org/iccvw/2019/chaudhary2019iccvw-ritnet/}
}