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Multiview Multitask Gaze Estimation With Deep Convolutional Neural Networks
IEEE Transactions on Neural Networks and Learning Systems
|September 6, 2018
Summary
This study introduces a new multiview method for gaze estimation, improving accuracy by simultaneously predicting gaze direction and point. It also presents the largest multiview gaze tracking dataset to date.
Area of Science:
- Computer Vision
- Human-Computer Interaction
Background:
- Gaze estimation predicts where a person is looking from eye images, crucial for understanding visual attention.
- Existing methods often use single cameras and focus on either gaze point or direction, not both.
Purpose of the Study:
- To develop a novel multitask method for accurate gaze point estimation using multiview cameras.
- To leverage the relationship between gaze direction and gaze point estimation.
Main Methods:
- Proposed a partially shared convolutional neural networks architecture for simultaneous gaze direction and point estimation.
- Introduced a new, large-scale multiview gaze tracking dataset with diverse subjects.
Main Results:
- The proposed multiview multitask approach consistently outperformed existing methods on multiple datasets.
- Demonstrated superior performance in gaze point estimation accuracy.
Conclusions:
- The novel multitask framework effectively integrates gaze direction and point estimation for enhanced accuracy.
- The new dataset facilitates further research and development in multiview gaze tracking.
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