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

被引：49

作者：

Chaudhary, Aayush K. ^{[1
]}

Kothari, Rakshit ^{[1
]}

Acharya, Manoj ^{[1
]}

Dangi, Shusil ^{[1
]}

Nair, Nitinraj ^{[1
]}

Bailey, Reynold ^{[1
]}

Kanan, Christopher ^{[1
]}

Diaz, Gabriel ^{[1
]}

Pelz, Jeff B. ^{[1
]}

机构：

[1] Rochester Inst Technol, Rochester, NY 14623 USA

来源：

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) | 2019年

关键词：

D O I：

10.1109/ICCVW.2019.00568

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 (1).

引用

页码：3698 / 3702

页数：5

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