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Tracking a detected face with dynamic programming

机译:动态编程跟踪检测到的人脸

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In this paper, we consider the problem of tracking a moving human face in front of a video camera in real-time for a model-based coding application. The 3D head tracking in a MBC system could be implemented sequentially as 2D location tracking, coarse 3D orientation estimation and accurate 3D motion estimation. This work focuses on the 2D location tracking of one face object through continuously using a face detector. The face detection scheme is based on a boosted cascade of simple Haar-like feature classifiers. Although such a detector demonstrated rapid processing speed, high detection rate can only be achieved for rather strictly near front faces. This introduces the 'loss of tracking' problem when used in 2D tracking. This paper suggests an easy method of solving the pose problem by using the technique of Dynamic Programming. The Haar-like facial features used in the 2D face detector are spatially arranged into a 1D deformable face graph and the Dynamic Programming matching is used to handle the 'loss of track' problem. Dynamic Programming matches the deformed version of the face graph extracted from a rotated face with the template taken online before 'loss of tracking' happens. Since the deformable face graph covers a big pose variation, the developed technique is robust in tracking rotated faces. Embedding Haar-like facial features into a deformable face graph is the key feature of our tracking scheme. A real time tracking system based on this technique has been set up and tested. Encouraging results have been got and are reported.
机译:在本文中,我们考虑了针对基于模型的编码应用程序实时跟踪摄像机前面移动的人脸的问题。 MBC系统中的3D头部跟踪可以顺序实现为2D位置跟踪,粗略的3D方向估计和准确的3D运动估计。这项工作专注于通过连续使用面部检测器来对一个面部对象进行2D位置跟踪。人脸检测方案基于简单的类似Haar的特征分类器的增强级联。尽管这种检测器显示出快速的处理速度,但是仅在相当严格的正面附近才能实现高检测率。当在2D跟踪中使用时,这就引入了“跟踪丢失”问题。本文提出了一种使用动态规划技术解决姿势问题的简便方法。 2D面部检测器中使用的类似Haar的面部特征在空间上排列成1D变形的面部图,动态编程匹配用于处理“轨迹丢失”问题。动态编程将从旋转的脸部提取的脸部图的变形版本与在线模板进行匹配,然后再进行“跟踪丢失”。由于可变形的面部图覆盖了很大的姿势变化,因此开发的技术在跟踪旋转的面部方面具有鲁棒性。将类似Haar的面部特征嵌入到可变形的面部图形中是我们跟踪方案的关键特征。已经建立并测试了基于该技术的实时跟踪系统。令人鼓舞的结果已经得到报道。

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