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Papers/RITnet: Real-time Semantic Segmentation of the Eye for Gaz...

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

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

2019-10-01Real-Time Semantic SegmentationSegmentationSemantic SegmentationGaze Estimation
PaperPDFCode(official)Code

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 https://bitbucket.org/eye-ush/ritnet/.

Results

TaskDatasetMetricValueModel
Semantic SegmentationOpenEDSmIOU95.3RITnet
10-shot image generationOpenEDSmIOU95.3RITnet

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