Optical flow constraints on deformable models with applications to face tracking
Optical flow constraints on deformable models with applications to face tracking
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DOI：
10.1023/a:1008122917811
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发表时间：
2000-07-01
影响因子：
19.5
通讯作者：
Metaxas, D
中科院分区：
文献类型：
--
作者：
DeCarlo, D;Metaxas, D
光流提供了对可变形模型的运动的约束。我们推导并求解一个动态系统，将流量作为硬约束，产生基于模型的最小二乘光流解。我们的解决方案还确保在与边缘信息相结合时仍然满足约束，这有助于对抗跟踪误差积累。可以使用卡尔曼滤波器来放松约束强制，这允许基于光流信息中存在的噪声的受控约束违反，并且使得能够更鲁棒且有效地组合光流和边缘信息。我们将此框架应用于使用3D可变形人脸模型的人脸形状和运动的估计。该模型使用少量的参数来描述丰富多样的面部形状和面部表情。我们给出了从图像序列中提取人脸形状和运动的实验，验证了该方法的准确性。他们还表明，我们的治疗光流作为一个硬约束，以及我们使用的卡尔曼滤波器来调和这些约束与光流中的不确定性，是至关重要的，以提高我们的系统的性能。
Optical flow provides a constraint on the motion of a deformable model. We derive and solve a dynamic system incorporating flow as a hard constraint, producing a model-based least-squares optical flow solution. Our solution also ensures the constraint remains satisfied when combined with edge information, which helps combat tracking error accumulation. Constraint enforcement can be relaxed using a Kalman filter, which permits controlled constraint violations based on the noise present in the optical flow information, and enables optical flow and edge information to be combined more robustly and efficiently. We apply this framework to the estimation of face shape and motion using a 3D deformable face model. This model uses a small number of parameters to describe a rich variety of face shapes and facial expressions. We present experiments in extracting the shape and motion of a face from image sequences which validate the accuracy of the method. They also demonstrate that our treatment of optical flow as a hard constraint, as well as our use of a Kalman filter to reconcile these constraints with the uncertainty in the optical flow, are vital for improving the performance of our system.