Real-time 3D Glint Detection in Remote Eye Tracking Based on Bayesian Inference
Real-time 3D Glint Detection in Remote Eye Tracking Based on Bayesian Inference
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DOI：
10.1109/icra.2018.8460800
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
David Geisler;D. Fox;Enkelejda Kasneci
中科院分区：
文献类型：
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作者：
David Geisler;D. Fox;Enkelejda Kasneci
由于人类凝视提供了关于我们的认知状态、动作和意图的信息，因此基于凝视的交互有可能实现流畅和自然的人机协作。在这项工作中，我们专注于可靠的视线估计在远程眼动跟踪的基础上校准免费的方法。虽然这些方法在受控设置中工作良好，但当照明条件改变或其他对象引起噪声时，它们会失败。我们提出了一种基于概率模型的新颖的自适应方法，该方法可以可靠地检测立体图像中的闪光，并使用包含光线和反射方面不同挑战的数据集来评估我们的方法。
As human gaze provides information on our cognitive states, actions, and intentions, gaze-based interaction has the potential to enable a fluent and natural human-robot collaboration. In this work, we focus on reliable gaze estimation in remote eye tracking based on calibration-free methods. Although these methods work well in controlled settings, they fail when illumination conditions change or other objects induce noise. We propose a novel, adaptive method based on a probabilistic model, which reliably detects glints from stereo images and evaluate our method using a data set that contains different challenges with regarding to light and reflections.