Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
复制标题
跟踪参与社交互动的人们的注视和视觉焦点
DOI：
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发表时间：
2017
影响因子：
23.6
通讯作者：
R. Horaud
中科院分区：
文献类型：
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作者：
Benoit Massé;Silèye O. Ba;R. Horaud
视觉注意焦点(VFOA)已被认为是一种重要的对话线索。我们对评估和跟踪与多方社会互动相关的VFOA感兴趣。我们注意到，在这种情况下，参与者要么彼此对视，要么看着感兴趣的物体；因此，他们的眼睛并不总是可见的。因此，凝视和视场估计都不能基于眼睛检测和跟踪。我们提出了一种利用眼睛凝视和头部运动之间的相关性的方法。在贝叶斯切换状态空间模型(也称为切换卡尔曼滤波)中，VFOA和凝视都被建模为潜在变量。所提出的公式导致了一种易于处理的学习方法和一种同时跟踪凝视和视觉焦点的高效在线推理过程。该方法使用两个公开可用的数据集Vernissage和LAEO进行测试和基准测试，这两个数据集包含典型的多方人-机器人和人-人交互。
The visual focus of attention (VFOA) has been recognized as a prominent conversational cue. We are interested in estimating and tracking the VFOAs associated with multi-party social interactions. We note that in this type of situations the participants either look at each other or at an object of interest; therefore their eyes are not always visible. Consequently both gaze and VFOA estimation cannot be based on eye detection and tracking. We propose a method that exploits the correlation between eye gaze and head movements. Both VFOA and gaze are modeled as latent variables in a Bayesian switching state-space model (also referred switching Kalman filter). The proposed formulation leads to a tractable learning method and to an efficient online inference procedure that simultaneously tracks gaze and visual focus. The method is tested and benchmarked using two publicly available datasets, Vernissage and LAEO, that contain typical multi-party human-robot and human-human interactions.
影响因子：
2.5
作者：
Freedman, EG;Sparks, DL
通讯作者：
Sparks, DL